Cross Asset Monitor Guide

Transcription

1 Cross Asset Strategy Cross Asset Monitor Guide How to read the Cross Asset Monitor This guide is a companion to our weekly Cross Asset Monitor. Most readers are likely to have in-depth expertise in some of the areas covered by the monitor but probably not all. By explaining how to interpret the information presented in the Cross Asset Monitor, this guide makes the entire publication accessible even to those investors who may not initially be familiar with all areas covered. 11 May 216 This guide is a companion to our weekly Cross Asset Monitor. The weekly monitor provides an overview of the development of different asset classes and helps to judge market sentiment. It monitors performance, volatility and correlation across asset classes, tracks a wide range of sentiment and macroeconomic indicators, global fund flows, speculative investor positioning and the major drivers of individual asset classes. Contents Performance Monitor... Correlation Monitor... 4 Volatility Monitor... 7 Sentiment Monitor... 9 Fund Flow Monitor CFTC Positioning Monitor... 1 Economic Surprise Monitor FX Monitor Central Bank & Inflation Monitor Sovereign Monitor... 2 Equity Monitor Credit Monitor Commodity Monitor Real Estate Monitor Emerging Markets Monitor... rd Edition For important disclosure information please see pages and 4. research.commerzbank.com / Bloomberg: CBKR / Research APP available Head of Cross Asset Strategy Dr Bernd Meyer, CFA dr.bernd.meyer@commerzbank.com

2 How to read the Performance Monitor The Performance Monitor summarises the recent performance of numerous assets and asset classes. In addition to identifying EUR-based (and where appropriate, USD or local currency) total returns for various assets, the performance monitor also identifies recent developments in a number of risk perception indicators. The cross asset return chart gives a top-down overview of recent asset class performance. In addition to EURbased returns, USD-based return data are included to give an indication of the effects of EUR/USD movements on total returns. The indices we use to represent the performance of the asset classes are listed in the performance monitor table (details on next page). Performance Monitor Last week, the risk-off environment continued on the back of ongoing global growth concerns, falling commodity prices and amid worries about the European banking sector. Bund and Treasury yields continued to decline. Sovereign debt and cash performed once again best, while risk assets strongly underperformed. Euro investors were additionally hit by the ongoing euro strength. Since the start of the year, the trade-weighted euro has added almost 4%. Within equity regions, Japan was the clear underperformer, suffering from the pronounced yen strength due to increasing safe haven demand. Overall, DM equities markedly beat EM stocks, in particular US and UK equities fared relatively well. Among commodities, precious metals and in particular gold gained strongly on safe haven demand and as investors price in less US rate hikes. Natural gas and crude oil fell on muted inventory data. Among credit, the outperformance of nonfinancials vs financials continued due to lacklustre Q4 reporting of financials and fears about non-performing loans. One week and year-to-date returns (%)* Cross Asset Global equity sectors Credit Commodities Equity REITs Credit Inflation linked debt Cash Sovereign debt Energy Materials Financials Industrial Cons. Discret. Inform. Techn. Telecom Cons. Staple Utilities Healthcare GBP non-financials GBP financials USD high yield EUR high yield EUR financials EUR non-financials EUR covered bonds USD non-financials USD financials FX changes versus EUR AUD GBP CNY CHF JPY USD EUR (TWI) Source: Bloomberg, Commerzbank Research EUR 1W EUR YTD USD YTD USD 1W EUR 1W EUR YTD USD YTD USD 1W EUR 1W EUR YTD Local YTD Local 1W W change YTD change Regional equity MSCI Lat. Am. MSCI EMEA Nikkei 225 MSCI EM Asia MSCI UK Shanghai SE (px) EuroStoxx 5 DAX STOXX 6 S&P 5 Sovereign debt GBP inflation linked UK govt EM (USD) Latin EM External debt (USD) EM (USD) Non-Latin EUR inflation linked JP govt USD inflation linked DE govt US govt Commodities Crude oil (WTI) Energy Copper Industrial metals Total Precious metals Gold Wheat Agriculture Livestock Natural gas Risk perception US 1Y yield DE 1Y yield EUR swp vol M5Y FX M impl vol itraxx Europe 5Y Markit CDX IG 5Y VDAX (pp) VIX (pp) EUR 1W EUR YTD Local YTD Local 1W EUR 1W EUR YTD Local YTD Local 1W EUR 1W EUR YTD USD YTD USD 1W *Please refer to table on page 4 for details of the instruments displayed W change (bp) YTD change (bp) The performance of global equity sectors is shown in this chart, quoted in both USD and euro terms. As with the other performance charts on this page, the series are sorted in order of one-week euro total return. The performance of sovereign debt, both nominal and inflation-linked, for various currency areas/issuers is summarised in the sovereign debt chart. Both EUR and local currency returns are displayed. A similar chart for credit includes performance data for investment grade corporate bonds financials and non-financials. The high yield performance for EUR and USD corporate bonds is shown as well. Commodity returns are based on total return (rather than spot price) indices. Total return indices represent the performance of future-based investment strategies and therefore include roll effects due to the slope of the futures curve. Developments in key EUR currency pairs are charted in the FX changes vs. EUR chart. A trade-weighted EUR index which provides a measure of the strength of the euro against the currencies of major trading partners is also included. The market monitor tracks risk perception indicators in order to provide an indication of developments in investor sentiment. In addition to European and US CDS spread indices, German and US 1Y bond yields are also tracked. The risk perception chart also displays changes in option implied volatilities for equities (by means of the VIX and VDAX indices), foreign exchange rates (as a M trade-weighted average volatility) and M options on 5Y EUR interest rate swaps (M5Y swaption volatility) May 216

3 Performance Monitor Top Movers The best and worst performing assets for various periods are highlighted in the top mover tables. Implied volatility indices and most individual commodities are omitted from these lists as these assets tend to be very volatile. The 12M Sharpe ratio table gives an indication of risk-adjusted performance. The Sharpe ratio is defined as excess return per unit of risk, and for positive excess returns, the higher the Sharpe ratio, the better the risk adjusted performance. Negative Sharpe ratios cannot be used to rank assets, i.e. we use the modified Sharpe ratio for ranking negative Sharpe ratios but do not show the actual numbers as the output range is very large. 12M EUR total return (%) YTD EUR total return (%) Top 15 Bottom 15 JP govt 1.8 Natural gas (TR) JPY (trade w eighted) 11.1 Crude oil (TR) EMBI+ Non-Latin 9.2 GSCI Energy (TR) US govt 6.4 Bovespa -8. EM External debt (USD) 6.2 GSCI Total (TR) -5.2 EFFAS Global 6.2 MSCI Latin America USD (trade w eighted) 6.1 Hong Kong REITs Consumer Staple 5.2 RJ/CRB Gold (TR) 4. Energy -24. iboxx $ financials.7 DJ-UBS -2.9 GSCI Precious Metals (TR). MSCI EMEA -2.8 US-inflation linked 2. Materials -22. DE govt 2. EM equity -2. Telecom 2.2 STOXX TMI Value EMBI+ Latin 1.9 MSCI EM Asia Local currency 2D z-score* Top 15 Bottom 15 JPY (trade w eighted) 2.99 GBP (trade w eighted) JP govt 2.84 UK REITs EFFAS Global 2.79 Nikkei DE govt 2.72 itraxx Europe 5Y TR US govt 2.61 itraxx Europe 5Y ER iboxx Covered 2.4 EONIA total return -2.8 MICEX (price index) 2.22 itraxx Crossover 5Y TR -2.4 UK govt 2.14 Financials -2.2 EMBI+ Non-Latin 1.91 Natural gas (TR) Gold (TR) 1.84 iboxx HY ex XO -1.8 EM External debt (USD) 1.62 Stoxx GSCI Precious Metals (TR) 1.61 Russell 2 (small cap) iboxx non-financials 1.5 Euro Stoxx VIX mid-term futures ETN 1.25 STOXX TMI Value iboxx corporates 1.2 Stoxx Top 15 Bottom 15 Gold (TR) 1.7 Natural gas (TR) GSCI Precious Metals (TR) 1.2 Crude oil (TR) -2.5 VIX mid-term futures ETN 1.2 Shanghai SE (price index) JPY (trade w eighted) 8. GSCI Energy (TR) JP govt 8.2 UK REITs DE govt.7 Financials EFFAS Global.2 Hang Seng EUR (trade w eighted) 2.6 Hong Kong REITs US govt 2. DAX -12. EFFAS Europe 1. Russell 2 (small cap) -12. iboxx Covered 1. Euro Stoxx iboxx non-financials 1.1 STOXX TMI Value -11. Utilities.7 GSCI Total (TR) EMBI+ Non-Latin.7 Stoxx USD (trade w eighted).7 STOXX Small EUR-based 12M Sharpe ratio** Top 15 Top 16- JP govt 1.52 EMBI+ Latin.1 JPY (trade w eighted) 1.7 iboxx $ corporates.1 USD (trade w eighted) 1.2 VIX mid-term futures ETN.1 EFFAS Global.85 EFFAS Europe.9 EMBI+ Non-Latin.67 Telecom.8 DE govt.5 iboxx Global Corporate.5 US govt.52 EONIA total return. iboxx Covered.5 iboxx $ non-financials neg. EM External debt (USD).44 iboxx financials neg. Gold (TR).41 Utilities neg. GSCI Precious Metals (TR). Inflation-linked neg. iboxx $ financials.29 itraxx Europe 5Y ER neg. Consumer Staple.26 iboxx corporates neg. US-inflation linked.18 itraxx Europe 5Y TR neg. EUR (trade w eighted).16 iboxx non-financials neg. Source: Bloomberg, Commerzbank Research *Distance from 2D moving average in stand. deviations;**mod. Sharpe ratio used for ranking of neg. Sharpe ratios The local-currency 2D z-score gives an indication of the level at which an index is trading relative to its 2- day moving average. In order to make values comparable across assets, we use a standardised measure. The z- score indicates, in standard deviations, the distance of the current level from the average value. That is: Level Current Average z score 2D 2D The modified Sharpe ratio delivers the same results as the Sharpe ratio if the excess return in the numerator is positive. However, the denominator is adjusted by using the standard deviation raised to the power of excess return divided by the absolute value of the excess return (ER). That is: Excess return Modified Sharpe ratio ER / abs( ER ) Performance Monitor Tabelle The performance table highlights changes in periods in both local currency and euro terms. The table also includes details of the benchmarks that we have chosen to represent the performance of various asset classes in the performance charts. Twelve-month highs and lows are displayed for each asset in order to put currently observed values in context. In addition to total returns, z-scores and the Sharpe ratio are displayed in the performance monitor summary table. Annualised 6M volatilities are shown for both local currency and EUR total returns. as at 12M 12M Ann. * Local Currency Total Return (%) Z-Score Ann. * EUR Total Return (%) Z-Score Sharpe Index CCY 4//16 Low High Vol (%) 1D 1W 1M M 12M YTD (2D)** Vol (%) 1D 1W 1M M 12M YTD (2D)** Ratio*** USD neg. arket equity USD 1,68. 1, , neg. DAX EUR 9, , , neg. Stoxx 6 EUR neg. Euro Stoxx 5 EUR,7.4 2,68.4, neg. S&P 5 USD 2,. 1, , neg. MSCI UK GBP 1,88.8 1, , neg. Nikkei 225 JPY 17, , , neg. USD , neg. MSCI EM Asia USD neg. MSCI EMEA USD neg. MSCI Latin America USD 2,9.7 1,58.7 2, neg. Shanghai SE (price index) CNY 2, , , neg. l USD May 216

4 How to read the Correlation Monitor The Correlation Monitor contains information about the degree of co-movement in the returns of assets. The level of correlation between assets and asset classes is an important input to the process of developing a cross asset strategy. Correlations are not static, so we must be aware of developments as they occur. The Pearson correlation coefficient is calculated by dividing the covariance of two assets by the product of their standard deviations. Coefficient values can range from +1, indicating a perfect positive linear relationship, to -1, indicating a perfect negative linear relationship. In the matrix, we show correlation coefficients in terms of percentages in order to reduce the number of digits displayed in each cell. Correlation Monitor Recent and long-term weekly return correlations 6-day correlation: risk assets vs safe-haven assets Rolling 12M correlation: EMBI Global vs Nikkei DAX vs Bunds S&P 5 vs US Treas. MSCI UK vs Gilts Nikkei 225 vs JGBs Rolling 12M Correlation of EMBI Glob. and Nikkei 225 (%) 1Y (bottom left) and 1Y less 1Y (top right) weekly return correlations with 1Y volatilities displayed on the diagonal DM equity DAX Stoxx 5 S&P 5 MSCI UK Nikkei 225 EM equity Bunds US Treas. Gilts JGBs EMBI Glob. Inflation link. iboxx corp iboxx corp iboxx $ corp High yield itraxx Europe 5Y TR itraxx XO 5Y TR GSCI Total GSCI Agriculture GSCI Energy GSCI Indust Metals GSCI Livestock GSCI Prec Metals FTSE REIT Dev. EONIA TR USD (tw) EUR (tw) EURUSD EURGBP EURJPY EURAUD VIX VIX mid-term futures VDAX Each week we display two charts, highlighting what we feel are interesting developments in the relationship between asset pairs. The first chart will show the 6-day rolling correlation of daily returns of risk asset vs. safe-haven asset pairs. The second chart will show the 12-month rolling correlation of weekly returns between asset pairs. DM equity DAX Stoxx S&P MSCI UK Nikkei EM equity Bunds US Treas Gilts JGBs EMBI Glob Inflation link iboxx corp iboxx corp iboxx $ corp High yield itraxx Europe 5Y TR itraxx XO 5Y TR GSCI Total GSCI Agriculture GSCI Energy GSCI Indust Metals GSCI Livestock GSCI Prec Metals FTSE REIT Dev EONIA TR USD (tw ) EUR (tw ) EURUSD EURGBP EURJPY EURAUD VIX VIX mid-term futures VDAX Source: Bloomberg, Commerzbank Research The bottom left part of the matrix displays correlations calculated from the last one year of weekly returns. This part of the matrix gives us an indication of the recent relationship between the returns of pairs of assets. The higher the value, the greater the tendency of the assets returns to move together in the same direction. Diagonal elements of the matrix contain one-year return volatilities for the assets. Weekly returns are used to calculate the correlation coefficients. When considering relationships between assets quoted on exchanges in different time zones, artificially low correlations can occur if daily return series are used. The top right part of the matrix displays the difference between the one-year correlation coefficient (from the bottom left part of the matrix), and a similar coefficient calculated from ten years of return data. This allows us to determine whether the current level of comovement between two assets is above or below the long-term level. As in the rest of the matrix, cells are colour coded to facilitate the identification of strong movements May 216

5 How to interpret the information in the Correlation Monitor Here we take excerpts from a recent Correlation Monitor and explain how one can extract information from the matrix. Although we limit our examples below to specific sections of the grid, the remainder of the Correlation Monitor also contains interesting information about the relationships between different asset classes. In the current environment of increased correlations within asset classes, it will become increasingly necessary to consider cross asset portfolios in order to benefit from diversification effects, and an understanding of developments in the co-movements of returns will prove to be most useful. First, note how the correlation between equity indices is very high. Diversification within an asset class is no longer as effective as it once was, and investors must increasingly resort to cross asset allocations in order to diversify their holdings. DM equity DAX Stoxx 5 S&P 5 By comparing the one-year correlation coefficient with that for a longer period (ten years), we see that correlations between equities and sovereign bonds are less negative than they have been in recent years. One reason for this change might be the low yield environment and the corresponding lower carry for sovereigns. A further discussion of such issues can be found in our Cross Asset Feature: Safe havens: where to seek shelter, 7 March 216. MSCI UK Nikkei 225 EM equity Bunds US Treas. Gilts JGBs EMBI Glob. Inflation link. iboxx corp iboxx corp iboxx $ corp High yield itraxx Europe 5Y TR itraxx XO 5Y TR GSCI Total By examining the hotspots in the top-right of the correlation heat map, we are able to identify asset pairs that currently have correlations significantly different from the long-term average. In this chart we use shorter-term correlations between risk assets and safe-haven assets to highlight shorterterm changes in risk sentiment. Each week we select one of three charts to highlight recent changes in the relationship between risk assets and safe-haven assets. GSCI Agriculture GSCI Energy GSCI Indust Metals GSCI Livestock GSCI Prec Metals FTSE REIT Dev. EONIA TR USD (tw) EUR (tw) EURUSD EURGBP EURJPY EURAUD VIX VIX mid-term futures VDAX DM equity DAX Stoxx S&P MSCI UK Nikkei EM equity Bunds DAX vs Bunds S&P 5 vs US Treas US Treas MSCI UK vs Gilts Nikkei 225 vs JGBs Gilts JGBs EMBI Glob Inflation link iboxx corp iboxx corp iboxx $ corp High yield May Rolling 12M Correlation of EMBI Glob. and Nikkei 225 (%) itraxx Europe 5Y TR itraxx XO 5Y TR Note how high yield debt tends to be equity-like, displaying a relatively high correlation to all of the equity indices in the matrix. As such, an allocation to high yield bonds can improve the diversification of a broad fixed income portfolio. Investment grade corporates are more closely related to government debt than is the case for high yield bonds. For higher quality credits, interest rates are generally more important as a driver of returns than for equity-like high yield bonds. High yield debt generally has a shorter duration and greater sensitivity to economic and corporate conditions. From the chart we can see that the correlation between EM debt and Japanese equities is approaching the top of the normal range. The exception to this is the period immediately following the credit crisis. Each week we will choose a different asset pair that might be of interest to investors. GSCI Total GSCI Agriculture GSCI Energy GSCI Indust Metals GSCI Livestock GSCI Prec Metals FTSE REIT Dev EONIA TR USD (tw ) EUR (tw ) EURUSD EURGBP EURJPY EURAUD VIX VIX mid-term futures

6 How to interpret the information in the Correlation Monitor (cont d) itraxx total return indices generate a return consistent with selling protection, and investing in money market instruments. By examining the correlation matrix, we can see that these CDS indices are highly correlated with equities. Because CDS do not have the same sensitivity to interest rates that corporate bonds have (duration), CDS indices are driven predominantly by credit spreads, and therefore have a relatively low correlation with corporate debt. MSCI World DAX EuroStoxx 5 S&P 5 FTSE 1 Nikkei 225 MSCI EM German Govt US Treasuries Gilts Japanese Govt EMBI Global Inflation Linked iboxx Corporates iboxx Corporates iboxx $ Corporates High Yield itraxx Europe 5Y TR itraxx Crossover 5Y TR iboxx Corporates iboxx Corporates iboxx $ Corporates High Yield itraxx Europe 5Y TR itraxx Crossover 5Y TR MSCI World DAX EuroStoxx 5 MSCI World DAX EuroStoxx S&P FTSE Nikkei GSCI Total GSCI Agriculture GSCI Energy GSCI Industrial Metals GSCI Livestock GSCI Precious Metals The very strong negative correlation between equity returns and volatility indices is clear from the Correlation Monitor. In a previous report (Cross Asset Feature: Volatility as an asset class, 15 April 21) we described various instruments that allow investors to gain static exposure to future volatility. Volatility products offer a means of diversifying a portfolio, as well as allowing speculation on the nature of future market conditions. DM equity DAX S&P 5 Stoxx 5 FTSE 1 Nikkei 225 S&P 5 GSCI Total MSCI UK GSCI Agriculture Nikkei 225 GSCI Energy FTSE REIT Dev. DM equity DAX Stoxx S&P MSCI UK Nikkei FTSE REIT Dev GSCI Industrial Metals GSCI Livestock GSCI Precious Metals The top right region of the matrix demonstrates how commodity returns are currently much more strongly correlated with equity returns than has been the case in previous years (livestock is an exception). Commodities have come to be treated as an asset class in their own right, and speculation in commodity prices has become more common. Commodity ETFs have seen very strong inflows of funds in recent years. In addition to these factors, the influence of the Chinese economy on both equity returns and demand for commodities has acted to increase correlations. DM equity DAX Stoxx 5 S&P 5 MSCI UK Nikkei 225 VIX VIX mid-term futures DM equity VDAX DAX Stoxx S&P MSCI UK Nikkei VIX VIX mid-term futures VDAX Looking at the correlation coefficients for real estate investment trusts (REITs) and equity, we see that we can expect a strong co-movement of returns. This is not surprising since the FTSE EPRA/NAREIT Developed index is designed to track the performance of listed real estate companies as well as REITs worldwide. In many cases, REITs can be considered as an equity sector, the performance of which depends on the success of the underlying real estate companies May 216

7 How to read the Volatility Monitor The Volatility Monitor contains information on the level and development of volatilities implied by the option markets on equities, oil, currencies and interest rate swaps. The expected volatility of the underlying asset over the life of an option is an important determinant of the value of the derivative. Observed option prices allow the extraction of a measure of expected volatility of the underlying asset, the so-called implied volatility. All volatilities are shown here in annualised terms. Volatility Monitor Implied vols rose across the board last week as global growth concerns intensified and as worries about the robustness of the European banking industry increased. Oil implied vol climbed the most and hit with 79 a fresh 12-month high. However, all equity implied vols that we monitor and also almost all fx and swaption implied vols gained. As a consequence, the normalised volatilities for all asset classes bar swaption stand above their 1-year averages. However, all swaption implied vols bar USD 5Y rose markedly, in particular EUR and GBP swaption implied vols climbed. The VIX futures curve shifted once again higher, with the curve remaining almost flat from the 2 nd to the 6 th future. The curve is still flatter than normal as investors seem to expect no further escalation of the financial turmoil. The weighted fx implied volatility average rose 1.pp last week, as the implied vols for all currency pairs increased, the most for USDJPY and EURJPY on continuing yen strength despite the surprise rate cut by the BoJ in recent weeks. Implied volatilities and implied volatility skews Equity, oil, fx and swaption implied volatilities as at 4//216 12M low 12M high 1Y avg* 1W chg 4W chg YTD chg Equity and oil implied volatility (%) and equity implied volatility skew (pp) VIX VDAX VSTOXX ivol Skew SPX ivol Skew SX5E OVX (Oil) FX implied volatility (M, %) EURUSD USDJPY GBPUSD AUDUSD USDCAD USDCHF EURJPY EURGBP USDHKD EURCHF Weighted avg Swaption implied volatility (M, bp, annualised, normalised) EUR 5Y EUR 1Y USD 5Y USD 1Y GBP 5Y GBP 1Y JPY 5Y JPY 1Y Normalised implied volatilities Realised volatilities (%) Steepness of VSTOXX and VIX futures curves (%) Equity implied volatility skew (M, 9%-11%) FX implied and realised volatility (%) Spot 1st future 2nd future rd future 4th future 5th future 6th future VSTOXX; dotted line avg since 29 for 2% of the obs. around current VSTOXX VIX; dotted line avg since 25 for 2% of the observations around current VIX 1Y average Weighted G1 6D realised FX vol Weighted G1 M implied FX vol Divergence from 1Y avg* in standard deviations -2. 1% Commodities Sovereign debt EUR inflat. linkers 9% 8% 7% 6% EUR high yield Equity implied vol** FX weighted implied vol EM equities REITs DM equities G1 Fx IG credit Oil implied vol Swaption vol*** D realised annualised volatility 1Y average 5Y avg 5% EuroStoxx 5 M 9-11 implied vol skew 1D average 5Y and 1Y euro swaption M implied volatility (bp) Y average 2 5Y EUR Swaption M impl. vol. 1Y EUR Swaption M impl. vol. The table provides information on the current level of equity, oil, fx and swaption implied volatilities, the respective 12M highs and lows, 1Y averages as well as 1W, 4W and YTD changes. The same information is also given for the equity implied volatility skew of the Euro Stoxx 5 and the S&P 5. Apart from the oil implied volatility, these data are explained in detail in other call-outs on this and the following page. Oil implied volatility is measured by the OVX index, the CBOE crude oil volatility index. It was the first commodity-based volatility index and reflects implied volatilities of US Oil Fund options with D maturity. The US Oil Fund is an exchange-traded security designed to track changes in crude oil prices. The normalised implied volatility chart shows equity, oil, fx and swaption implied volatilities in a comparable format. Each time series is normalised by subtracting the 1Y average and dividing by the 1Y standard deviation. The chart shows that implied volatilities of different asset classes tend to be highly correlated, as they are essentially impacted by the same macro factors. While volatilities across asset classes differ temporarily, they usually follow the same regime, as shown in the chart. In a Cross Asset Feature: Volatility as an asset class, 2 April 21, we showed that equity implied volatility and the average volatility of other asset classes have shown a correlation of.78 since 199. Source: Bloomberg, Commerzbank Research *For oil since May 27; **avg VIX, VDAX & VSTOXX, ***avg 1Y EUR, USD, GBP, JPY FX implied volatility is measured by the implied volatility of at-the-money (ATM) currency options with M maturity. The weighted average is based on the 1 most liquid currency pairs (shown in the table), using their proportion in spot trading according to the BIS Triennial Central Bank Survey 21. Swaption implied volatility measures the expected volatility of interest rates over the maturity of the swaption and is typically shown in basis points. A swaption is an option granting its owner the right to enter an underlying swap. Although options can be traded on a variety of swaps, the term swaption typically refers to options on interest rate swaps. The most common interest rate swap is one where one counterparty pays a fixed rate (the swap rate), while receiving a floating rate (usually linked to a reference rate such as LIBOR). We show volatilities implied by swaptions with M maturity referring to 5Y and 1Y interest rate swaps in four currencies. 11 May 216 7

8 s global growth concerns intensified and as worries about the robustness of plied vol climbed Cross Asset the Strategy most and Cross hit with Asset 79 Monitor a fresh Guide 12-month high. However, o almost all fx and swaption implied vols gained. As a consequence, the waption stand above their 1-year averages. However, all swaption implied R and GBP swaption implied vols climbed. How to read the Volatility Monitor (cont d) er, with the curve remaining almost flat from the 2 nd to the 6 th future. The m to expect no further escalation of the financial turmoil. 1.pp last week, as the implied vols for all currency pairs increased, the Implied volatility skew is the pattern of implied volatility for en strength despite the surprise rate cut by the BoJ in recent weeks. different strike prices. For equity indices, implied volatility tends to decline with rising strikes. This is primarily because: Normalised (1) if investors implied are risk volatilities averse, they are willing to pay a higher chg YTD chg 2. premium Divergence for from downside 1Y avg* protection than for upside potential; 1.5 in standard deviations and (2) as both individual security volatility AND return correlations.5 tend to rise in falling markets, index volatility rises. particularly strongly. This is what the market is pricing in with higher implied volatilities for lower strikes. The skew itself is not constant over time. The strength of the skew as well as its development allows one to judge investor sentiment Equity implied vol** FX weighted implied vol Realised volatilities (%) Commodities EM equities REITs DM equities G1 Fx Sovereign debt IG credit EUR inflat. linkers EUR high yield Oil implied vol Swaption vol*** D realised annualised volatility 5Y avg ) Equity implied volatility skew (M, 9%-11%) 1% The chart depicts the difference between the implied volatility of Euro Stoxx 5 options with M maturity and a strike price of 9% and 11% of the current index level. Over the last 1 years, implied volatility was on average 7.7pp higher for options with a 9% strike than for options with a 11% strike. An increasing skew and levels above the long-term average signal (rising) investor bearishness, as investors are willing to pay a higher premium for downside protection relative to upside calls than previously, or relative to the long-term average. e 6th future rrent VSTOXX current VIX Jan-16 ied FX vol The 9% chart illustrates realised volatilities of different asset classes, sorted by the current level of volatility. While the bar 8% reflects the current level of volatility, the dot shows the 5-year 7% average. 6% Realised volatility is calculated 1Y averageby using the standard deviation 5% of the ln returns of the last 6 trading days, annualised by the square root of 25. EuroStoxx 5 M 9-11 implied vol skew 1D average 5Y We and show 1Y also euro the swaption 5-year M average implied of volatility the 6-day (bp) realised volatility 11 to analyse where the volatility is relatively high or low compared 1 to the own history The chart depicts 1Y average the current shape of the VSTOXX and VIX futures curves, as well as their normalised shape. The 5Y EUR Swaption M impl. vol. 1Y EUR Swaption M impl. vol. *For oil since May 27; **avg VIX, VDAX & VSTOXX, ***avg 1Y EUR, USD, GBP, JPY Steepness of VSTOXX and VIX futures curves (%) Spot 1st future 2nd future rd future 4th future 5th future 6th future VSTOXX; dotted line avg since 29 for 2% of the obs. around current VSTOXX VIX; dotted line avg since 25 for 2% of the observations around current VIX steepness of the future determines the roll losses/gains of going long volatility by buying VIX futures. The steeper the 2 curve Jan-1the higher Jul-1 the Jan-14 loss a Jul-14 volatility Jan-15 future long Jul-15position Jan-16would cause all else being equal. As a volatility long position tends to provide a good hedge against the tail risk in portfolios, the steepness of the volatility futures curve can be interpreted as the cost of downside protection. The flatter the curve, the less investors are willing to pay for downside protection, similar to the interpretation of a low level of implied volatility skew. A combination of low skew, low implied volatility and a relatively flat volatility future curve might be a signal of investor overconfidence. Equity imp 1% 9% 8% 7% 6% 5% Jan-1 E For example, the VIX curve is normalised by taking the average of the VIX futures level for 2% of the historical VIX observations closest to the current level of volatility. Data for the whole curve are available from 25 for the VIX and from 29 for the VSTOXX. By comparing the recent curve with the normalised curve, investors can gauge if the curve is particularly flat or steep for the given level of volatility. As volatility tends to be mean-reverting, an extreme curve would indicate some normalisation going forward May 216

9 How to read the Sentiment Monitor Sentiment Monitor The Sentiment Monitor gives an overview of key market sentiment data obtained from investor surveys. We have shown in a Cross Asset Feature 1 that these data can guide investment decisions but investors need to know how to read and interpret them. For our Sentiment Monitor, we use data from the German survey provider Sentix and from the American Association of Individual Investors (AAII). 1 Survey-based sentiment indices Investor sentiment: what it tells us and how to trade on it, 5 September 211 Like in the previous week, Sentix equity sentiment improved further for the short-term while the bearish medium-term sentiment remained almost unchanged. Short-term indices now reached bullish quintiles for most indices, resulting in sell signals for German and European stocks. The AAII bull-bear index remains in the bearish 2 nd quintile. Ahead of the ECB meeting, investors are taking profits in bonds, pushing their short-term sentiment markedly lower into most bearish 1 st quintiles. Our Sentimeter readings therefore show now buy signals for bonds, in particular for Treasuries. USD/JPY short-term sentiment increased, leading to a slightly negative Sentimeter reading for the currency pair. Oil was supported by news that Russia will host a meeting to renew talks on capping oil output and the already bullish short-term oil sentiment therefore improved further. Despite markets in risk-on mode, gold is still trending higher, leading to increased sentiment indices with at the same time very low levels of uncertainty. Sentiment indices Overview as at 5//216 Bull-Bear (%) Neutrality (%) Indicator Level 1W chg Quintile* 4W avg Level Quintile* Sentix DAX 1M () (5) Sentix DAX 6M (1) () Sentix Euro Stoxx 5 1M () (5) Sentix Euro Stoxx 5 6M (1)..7 5() Sentix S&P 5 1M (4) (4) Sentix S&P 5 6M (1) () Sentix CSI 1M** (2) (4) Sentix CSI 6M** (1) () AAII Bull-Bear (2) () AAII Bull 2..8 (2) 27.5 AAII Bear (4) 6.8 Sentix Bund Future 1M () () Sentix Bund Future 6M (4) () Sentix T-Bond Future 1M (4) () Sentix T-Bond Future 6M (5) (4) Sentix Dollar vs Yen 1M (1) (5) Sentix Dollar vs Yen 6M (1) (4) Sentix Euro vs Dollar 1M (4) (5) Sentix Euro vs Dollar 6M (5) (5) Sentix Oil 1M (5) () Sentix Oil 6M (5) (1) Sentix Gold 1M (5) (1) Sentix Gold 6M (5) (1) * e.g. 1 denotes quintile of 2% lowest values in 2 year rolling history. Quintiles from the previous week are shown in brackets.**china A-Shares (since Oct '9) Sentix Euro Stoxx ,5 Mar-11 Mar-12 Mar-1 Mar-14 Mar-15 Mar-16 Sentix EUR/USD ,,5, 2,5 2, Euro Stoxx 5 (rhs) Sentix 1M Euro Stoxx Sentix 6M Euro Stoxx Mar-11 Mar-12 Mar-1 Mar-14 Mar-15 Mar-16 EUR/USD (rhs) Sentix 1M EUR/USD Sentix 6M EUR/USD Sentimeter: signals for future asset performance Equity negative DAX EuroStoxx Currencies negative EUR/USD USD/JPY Sovereign Fixed Income Commodities Note: Indication based strategies described in the sentiment Cross Asset Feature (1/11/211); for currencies and gold based on bull-bear index, for equities, fixed income and oil also based on neutrality index Sentix Bund Future AAII Sentiment Survey positive 1W ago 1W ago positive 1W ago 1W ago negative negative Bund Treasury Gold Oil positive 1W ago 1W ago positive 1W ago 1W ago Mar-11 Mar-12 Mar-1 Mar-14 Mar-15 Mar Bund Future (rhs) Sentix Bund Future 1M Sentix 6M Bund Future -4 1, Mar-11 Mar-12 Mar-1 Mar-14 Mar-15 Mar-16 S&P 5 (rhs) AAII Bull-Bear ,2 2, 1,8 1,6 1,4 1,2 There are four possible responses in the Sentix survey: bullish, neutral, bearish and don t-know. To get a better indication of market sentiment we calculate a net bulls figure using a method similar to that employed by Sentix, i.e. and # Bulls # Bears # Bulls # Bears # Neutral # Neutral # Bulls# Bears# Neutral for the number of participants expecting a sideways market (Neutrality index). For AAII a don t-know answer is not available. For details on the Neutrality Index please see the next page. As all of the sentiment indices tend to be biased (e.g. equity sentiment is bullish on average), we rank the levels in quintiles to make them comparable across assets. This is accomplished by sorting the data points and dividing them into five equal-sized bins. The first quintile contains the lowest 2% of index levels and represents the most bearish views, while the fifth quintile contains the highest 2%, representing the most bullish readings. The quintiles are calculated using two-year rolling windows in order to deal with possible changes in the market environment. Quintiles from the previous week are shown in brackets. Source: Bloomberg, Commerzbank Research The AAII Sentiment survey focuses on private investors and has over 15, registered members, of which c. 4 participate in the weekly survey. It determines the percentage of individual investors who are bullish, bearish, and neutral on the US stock market for the next six-month period. The poll has been conducted since The survey asks if participants feel that the direction of the stock market over the next six months will be up, down or sideways. The poll window is from Thursday to Wednesday and the results are published each Thursday. We find that the bear index level proves useful as a momentum indicator, while the AAII neutrality index shows a negative relationship with future returns. Sentix surveys c.,1 registered investors, of which over 7 are institutional investors. The survey is carried out on a weekly basis on Fridays and Saturdays and the indices are published each Sunday evening. Since the beginning of the survey in February 21, Sentix investors have been surveyed on their short-term (one-month) and medium-term (six-month) expectations for a number of markets. 11 May 216 9

10 How to read the Sentiment Monitor (cont d) Sentix Bund Future Mar-11 Mar-12 Mar-1 Mar-14 Mar-15 Mar-16 Bund Future (rhs) Sentix Bund Future 1M Sentix 6M Bund Future The neutrality index identifies the proportion of investors expecting the market to stay at its current level for the respective timeframe (one month or six months). The proportion of investors that expect the market to trade roughly sideways tends to be inversely related to future asset performance. This relationship holds true for equity, bond and oil sentiment both for the short-term and the mediumterm survey horizon. We cannot verify it for currencies and gold. Our Cross Asset Feature 2 from 1 November 211 shows that high uncertainty is more likely to be followed by a market downtrend than an uptrend and that this relationship can be used to improve the simple short-term contrarian and medium-term momentum strategies as shown in the chart to the right Charts available on the Sentiment Monitor page illustrate how one-month sentiment and six-month sentiment, i.e. the net amount of bullish readings for one and six month expectations, develops over time for various assets (this chart shows Bund future sentiment). Similar charts are available for the Euro Stoxx 5, EUR/USD and the AAII survey for the US equity market. We find that for surveys that separately ask about views for two different time horizons (such as the Sentix survey), the short-term sentiment measure tends to reflect investors current positioning and therefore can be used as a contrarian indicator. Medium-term sentiment tends to reflect investors expectations of future asset performance, independent of their current positioning, and is useful as a momentum indicator. Combining short-term contrarian and medium-term momentum signals results in consistent outperformance in our backtests across all assets covered by the Sentix survey. There is a home bias in investor sentiment surveys; surveys for assets closer to the participants hearts tend to have greater predictive power. Performance backtest of strategy on short- and medium-term sentiment signals with neutrality overlay vs. 5/5 Euro Stoxx 5/cash benchmark Survey-based sentiment - an update: Neutral sentiment reflects uncertainty beware of downside risks if too many investors expect a sideways movement, 1 November Performance for cross asset strategy based on DAX and Bund Sentimeter signals Strategy Benchmark Strategy/BM (rhs) Euro Stoxx 5 Benchmark Euro Stoxx 5 Strategy Euro Stoxx 5 Strategy/Benchmark (rhs) Our Cross Asset Feature from 27 February 215 shows how signals from our Sentimeters can successfully be applied in a cross-asset context. Signals from sentiment surveys: The road test passed let s go cross asset!, 27 February 215 Our proprietary Sentimeters summarise the message contained in survey sentiment. We translate our findings into a single figure by firstly combining short-term contrarian and medium-term momentum sentiment signals, and secondly, for equity, oil and fixed income, adding a neutrality overlay to this signal. The Sentimeters indicate the degree of support from sentiment surveys for each asset class. Our Cross Asset Feature shows that this approach works for all asset classes and that the information ratio of strategies based on these signals are significantly positive in most cases. Please be aware that a positive Bund future Sentimeter has to be interpreted as indicating an increased probability of rising bond prices and declining yields. negative DAX EuroStoxx positive 1W ago 1W ago 1 11 May 216

11 How to read the Fund Flow Monitor The Fund Flow Monitor gives an overview of trends in fund flows for asset classes and geographic regions. In our Cross Asset Feature from 18 June 21, The value of weekly fund flow data, we presented backtests showing how one can use 4-week average inflows to generate trading signals that lead to significant outperfomance. Flows are shown as a percentage of AuM (assets under management), as changes of coverage across regions and asset classes, fx-rates and categorisation of individual funds over time may affect flows measured in absolute terms in US dollars. Flows shown here include ETF flows. In our Cross Asset Feature of 1 December 211, The value-added of ETF flows and new fund flow data, we show that ETF flows improve the performance of our model. Fund Flow Monitor Aggregated asset class inflows added up to $17bn. While equity funds suffered strong outflows of $6.8bn, money market funds witnessed inflows of about $24bn. Commodities enjoyed strongest inflows in more than a year. $2.2bn were added to commodity funds in the recent week. Again balanced funds revealed outflows. Bond funds were virtually unchanged. In the bond space government, municipals and mortgage funds continue to enjoy strong inflows. This underpins again the risk-off pattern seen in the last weeks. Moreover high yield, bank loan and credit funds still suffered considerable outflows of $4bn in total. EM bond funds outflows together were more than $1bn as all three currency categories had losses. European equity funds showed the strongest outflows since August last year. Almost $2bn were redeemed. Also Japanese equity funds posted outflows last week while the 4-week average flow is still the strongest among the regions. While GEM revealed the 15 th week of outflows in a row, LatAm surprised with inflows. EPFR fund flow data AuM *, streak of flows **, z-score of 1w/4w avg flows *** Bond flows by type (% of AuM) Fund flows by asset class (% of AuM) as at 2//216 AuM Streak AuM Streak 1W 4W 1W 4W Equities 7,169 1 Bonds,578 1 DM equity 6,461 1 DM bonds, 1 Commodity Int. equity 1,421 2 Int. bonds Pacific 52-1 US bonds 1,996 9 Money market Europe 1,19-4 EU bonds Japan 2 Credit 84 2 US bonds US equity,65-9 Government 74-2 EM bonds EM equity 77 1 Credit/Government 1,9-2 GEM 51 1 Inflation 81 Balanced EM Asia 8-2 Municipals Lat Am 18 4 Bank Loan Bonds EMEA 2 High yield 44 2 Global sector 927 Total return 77-8 DM equity Materials 15 8 Mortgage Consumer 59-1 EM bonds Equities Energy 99-1 Blended currency 4-2 Financials 8 1 Local currency EM equity Health care 9 1 Hard currency 97 2 Infrastructure Others EU bonds Real estate Money market,764-1 Technology 66-4 Balanced Telecom 5 4 Alternative Utilities 41 8 Commodity W avg weekly flows excl. ETF flows ytd (top) High yield Mortgage Municipals Inflation Credit Government Total return Equity flows by sector (% of AuM) Telecom Materials Financials Utilities Health care Real estate Infrastructure Energy Emerging Portfolio Fund Research (EPFR, tracks funds and ETFs on a global basis. It covers funds registered for sale in most major DM jurisdictions and offshore domiciles, including the US, the UK, Switzerland, Australia, Hong Kong, Germany, France and others. The data are available on a weekly basis and are organised into either geographic or asset class categories. The flows presented here are net flows for various funds and fund groups, excluding portfolio performance and currency fluctuations. EPFR releases data each Thursday at 5pm (EST) for the prior Thursday to Wednesday period. For details of the EPFR coverage universe, please see the next page. Credit/Government Bank Loan avg weekly flows (top) 4W ytd Equity flows by region (% of AuM) Int. equity Lat Am GEM Consumer Technology W avg weekly flows (top) ytd 52-week cumulative equity fund flows by asset class A set of charts displays net weekly, 4- week average and year-to-date flows for asset classes, equity sectors and equity regions. EMEA Japan US equity EM Asia Europe W avg weekly flows excl. ETF flows ytd (top) Source: EPFR, Commerzbank Research Mar-15 May-15 Jul-15 Sep-15 Nov-15 Jan-16 Equities Commodities Bonds Balanced Money Market * Assets under Management (US$ bn), ** weeks with steady flows, *** based on recent 52 weeks, capped at ±1.5 The bond flows by type chart shows the net flow into DM bond funds, split into several types with individual risk profiles. Floating rate loans for example bear a much greater risk than government debt. The table summarises the main developments in each fund group. It presents the assets under management, the 4-week average flow and year-to-date fund net inflows in equity, bond, money, balanced and alternative markets. Inflows into a particuar asset class increase demand and therefore tend to drive that asset s price higher. Strong performance often attracts additional inflows, so fund flows tend to show some inertia and contain predictive power for future performance. Net flows into equity funds, and EM equity in particular, are an important indicator of investors risk appetite. In contrast, when risk aversion rises, flows into DM bonds and money market funds increase as these assets act as a safe haven. The 52-week cumulative flows for equity funds by region present the aggregated net flows and hence indicate whether, in the long run, investors are moving into a region, or whether capital is being withdrawn. 11 May

12 How to read the Fund Flow Monitor (cont d) as at 2//216 AuM Streak 1W 4W AuM Streak 1W 4W Equities 7,169 1 Bonds,578 1 EPFR covers daily, weekly and monthly flows. For our DM equity 6,461 1 DM bonds, 1 Commodity Int. equity 1,421 2 Int. bonds analysis and for the Cross Asset Monitor, we focus on Pacific 52-1 US bonds 1,996 9 Money market Europe 1,19-4 EU bonds weekly fund flow data. Japan 2 Credit 84 2 US bonds US equity,65-9 Government 74-2 On a weekly basis, equity fund flows are individually EM equity 77 1 Credit/Government 1,9-2 EM bonds GEM 51 1 Inflation 81 reported for EM, DM and global sector funds. Investor EM Asia 8-2 Municipals Balanced Lat Am 18 4 Bank Loan interest in sector funds is increasing. Although some of EMEA 2 High yield 44 2 Bonds Global sector 927 Total return 77-8 the sectors do not have a significant size at the DM equity Materials 15 8 Mortgage moment, flows of material funds investing in mining Consumer 59-1 EM bonds Equities Energy 99-1 Blended currency 4-2 and oil companies, together with real estate funds, Financials 8 1 Local currency EM equity Health care 9 1 Hard currency 97 2 should contain significant information. Infrastructure Others EU bonds Real estate Money market,764-1 Within EM equity, the breakdown includes Asia ex- Technology 66-4 Balanced Telecom 5 4 Alternative Japan, LatAm, EMEA (emerging Europe, Middle East Utilities 41 8 Commodity W avg weekly flows excl. ETF flows ytd (top) and Africa), as well as global EM (GEM). The GEM classification includes BRIC and all non-regional funds. Money market 25% Balanced 5% Funds by asset class covered by EPFR Equities 47% as at 2//216 AuM Streak 1W 4W AuM Streak Equities 7,169 1 Bonds,578 1 DM equity 6,461 1 DM bonds, 1 Int. equity 1,421 2 Int. bonds Pacific 52-1 US bonds 1,996 9 Europe 1,19-4 EU bonds Japan 2 Credit 84 2 US equity,65-9 Government 74-2 EM equity 77 1 Credit/Government 1,9-2 GEM 51 1 Inflation 81 EM Asia 8-2 Municipals Lat Am 18 4 Bank Loan EMEA 2 High yield 44 2 Global sector 927 Total return 77-8 Materials 15 8 Mortgage Consumer 59-1 EM bonds Energy 99-1 Blended currency 4-2 Financials 8 1 Local currency Health care 9 1 Hard currency 97 2 Infrastructure Others Real estate Money market,764-1 Technology 66-4 Balanced Telecom 5 4 Alternative Utilities 41 8 Commodity Bond funds are divided into EM, US and non-us categories. As EM bond funds tend to have a currency rather than a country focus, a breakdown by blended, local and hard curreny is shown. Useful data, however, are available only since 29. For DM funds, a breakdown by asset class is available, where high yield and floating rate loan funds invest in more risky bonds. The coverage of EPFR is only a sample of the money invested in the whole fund industry. History is available back to October 2 for most categories. As money market fund flows are available starting only in January 27, these data are of limited help for historical studies. 1W 4W Commo Money ma US bo EM bo Balan Bo DM eq Equ EM eq EU bo Bonds 2% DM equity funds are dominated by US equity funds. International (global equity funds) do not invest in a specific region, but look for opportunities on a global basis. Lat Am % US equity 56% Int. equity 22% Pacific ex Japan 1% Europe 16% GEM 5% EM Asia 4% DM equity funds covered by EPFR Japan 5% EM equity funds covered by EPFR EMEA 4% In EM equity funds, non-regional funds (which can invest in a wide selection of EM) represent nearly 5% of the whole market May 216

13 How to read the CFTC Positioning Monitor The Commitments of Traders Reports (COT) is published by the Commodity Futures Trading Commission (CFTC) and provides a breakdown of each Tuesday's open interest for markets in which 2 or more traders hold positions equal to or above the reporting levels established by the CFTC. The weekly report is released every Friday at :PM EST subject to the CFTC release schedule. Positions are separated for non-commercial and commercial traders. A non-commercial trader takes positions that are purely speculative. A commercial trader is engaged in business activities hedged by the use of the futures or options markets. The CFTC Positioning Monitor focuses on the net future position (long positions less short positions) of non-commercial traders, to see how speculative investors are positioned. CFTC Positioning Monitor Changes in CFTC positioning (from Tuesday to Tuesday) show some signs of relief with selling in US Treasury futures and buying in the S&P 5 future. However, positioning changes were small; the S&P 5 position remains considerably net short and also strong buying in precious metals does not support a pronounced risk-on picture. US Treasury futures were sold on aggregate, though the 5Y maturity bucket saw considerable buying. The S&P 5 net short position was reduced by c. 1% and the VIX net long position declined by 12%, though the Nasdaq 1 future still saw selling pressure and is now close to a neutral position. Energy and precious metals saw considerable buying last week, the gold net long position increased by 5%. Agriculturals meanwhile faced strong selling pressure for all but coffee futures. CFTC* net non-commercial futures positions CFTC net non-commercial futures positions JPYUSD AUDUSD 9/2/216 1Y high 1Y low 1W chg 4W chg M chg share (%) 1 EUR GBP JPY CHF CAD AUD NZD MXN USD (in $bn) Y T-Notes Y T-Notes Y T-Notes Long T-Bonds Ultra long T-Bonds Total Bonds S&P NASDAQ Nikkei VIX Oil (WTI) Nat. Gas Gold Silver Platinum Palladium Copper (COMEX) Wheat (CBOT) Corn Soybeans Cotton Sugar Cocoa Coffee share of short and long pos. of non-commercial traders as % of total traders 2 duration weighted average consolidated Net position non-commercial traders USD/JPY (inv., rhs) Net position non-commercial traders AUD/USD (rhs) Aggregated USD positions (in $bn) EURUSD GBPUSD CADUSD Source: Bloomberg, CFTC, Commerzbank Research *Commodities Futures Trading Commission: Commitments of Traders report of open interest (in contracts) Net position non-commercial traders US$ trade weighted (rhs) Net position non-commercial traders EUR/USD (rhs) Net position non-commercial traders GBP/USD (rhs) Net position non-commercial traders USD/CAD (inv., rhs) For the S&P 5, Nikkei and NASDAQ 1 stock indices, and for WTI crude oil and soybeans, we present consolidated net position data. Net positions in different classes of futures that refer to the same underlying are aggregated after being adjusted for currency and contract size (following the methodology used by the CFTC, we adjust to the largest contract size). This ensures that traders who spread positions in the same underlying across different types of future and actually have no net exposure do not contribute, and that traders that for whatever reason only trade in either one of the types of future are considered. This approach gives a more complete overview of the current positioning of noncommercial traders in the market The table summarises the current net future positions of non-commercial traders for currencies, interest rate products, equity indices, volatility indices and various commodities. Current positions are put into historical perspective by columns showing the 1-year high and low, while 1W, 4W and M changes give an indication of the way in which net positions have developed recently. The share column shows the fraction of total open interest, expressed as a percentage, that noncommercial traders are currently responsible for (please see next page). In the fixed income space we also calculate a duration-weighted net position to judge whether investors have only reduced (increased) duration or whether they are generally short or long US government bond exposure. It is derived from the net positions for 2Y, 5Y and 1Y Treasury Note futures, and long and ultralong Treasury Bond futures. The charts generally illustrate the tendency of the price of the underlying security to rise as net short positions are reduced or net long positions are increased. Similarly, the price of the underlying security tends to decline when net long positions are reduced or net short positions increased. From that perspective, the change in net future positions can be interpreted as a momentum measure. However, large net long or net short positions can also be interpreted as a risk measure. Though such positions can persist for extended periods of time, the price of the underlying security often then reacts strongly, with unusually large net long positions ultimately leading to a strong decline of the price of the underlying asset and vice versa. 11 May 216 1

14 How to read the CFTC Positioning Monitor (cont d) The aggregated USD positions chart provides an overview of how the market is positioned vs. the US dollar. Data are presented in billions of US dollars and are calculated as a weighted average of each of the eight currencies for which data from the CFTC COT report are available (euro, British pound, Japanese yen, Swiss franc, Mexican peso and the Canadian, Australian and New Zealand dollars). Weighting the respective net positions (NP) means that the contract size (CS) and exchange rate (FX) as at Tuesday are taken into account. This figure is finally multiplied by -1: Gold Aggregated USD 1* i1 NP * CS * FX The resulting time series is plotted alongside a trade-weighted USD index that is based on a basket of seven currencies Australian dollar (1.%), British pound (4.1%), Canadian dollar (14.7%), euro (17.8%), Japanese yen (8.%), Swedish krona (1.%) and Swiss franc (1.5%). 1,9 1,7 1,5 1, 1,1 Net position non-comm. Money manager Gold ($/oz, rhs) i i i Net position non-commercial traders US$ trade weighted (rhs) On 4 September 29, the Commodity Futures Trading Commission also began publishing a disaggregated COT report. The first version of the report covered 22 major physical commodity markets; on 4 December 29, the remaining physical commodity markets were included. Rather than separating traders into commercial and noncommercial only, the disaggregated report classifies traders as: (1) Producers/Merchant/Processor/User; (2) Swap Dealers; () Managed Money; or (4) Other Reportables. For our purpose, one can argue that we should look at net positions of the Managed Wheat (CBoT) Money category. However, historical data for the 8 disaggregated COT report is only available back 1, to 1 June 26, the disaggregated COT report 9 is only available for commodities and the net position 8 pattern of the Managed Money category does not -2 7 differ substantially from the legacy non-commercial 6-7 category. We therefore stick to the traditional noncommercial category. To allow our readers to compare 212 current 21 developments in the 216Managed Net Money position non-comm. category, we Money provide manager the net Wheat position (USd/bu, rhs) for these as well, where available For oil, we show not only the disaggregated data of money managers as reported by the CFTC (see above for details), but we also display the money manager position in Brent oil futures traded at the ICE. Both positions are of the same contract size (1 barrels) and can show if e.g. the money manager positioning in Brent and WTI crude oil is developing in the same or in different directions, e.g. to show substitution effects. Oil (WTI) Net position non-comm. Money manager ICE Brent Money manager Oil (WTI, $/bbl, rhs) May 216

15 How to read the Economic Surprise Monitor The Economic Surprise Monitor tracks 89 macroeconomic indicators and provides information on whether recent data releases were above, in line with, or below consensus forecasts. This information is aggregated into four surprise indicators, which provide an overview of whether macro indicators tended to surprise to the upside or the downside over time. Historically, consistent surprises in individual data points tend to lead consensus revisions of the big picture. The surprise indicators are calculated for economic activity and inflation in both the euro area and the US. Economic Surprise Monitor Last week saw merely two relevant data releases in the EMU. While the ZEW surprised only slightly to the upside, the flash Consumer confidence in the EMU for Feb fell clearly short expectations. A reading of -8.8 followed a survey of Three of Last week s eight data releases in the US were below while four came out above consensus. The US surprise indicator rose somewhat. With an increase of.9% for January the Industrial production came out clearly above the consensus of.4%. With 262k the Initial jobless claims came out better than expected (275k est.). The final reading of the French HICP was released with -1.1% which was below the consensus of -1.%. The monthly EMU PPI was released with -.8% which was below the consensus of -.6%. In the US, all four price data releases came out above consensus. As a consequence our index soared significantly. With.% the core CPI came out above the consensus of.2%. Also the headline CPI for Jan contributed positively. Commerzbank Surprise * Indicators Surprise indicator for EMU activity GDP 21 (rhs) Indicator Surprise indicator for US activity Top 15 EMU surprises of the last four weeks ** Top 15 US surprises of the last four weeks ** Date EMU activity Period Wgt % Cons. Actual Surprise* 1//216 Unemployment rate - EMU Jan /2/216 Industrial confidence (ESI) - EMU Feb /2/216 Service confidence (ESI) - EMU Feb /2/216 Consumer confidence - IT Feb /2/216 Consumer confidence - FR Feb /2/216 Leading indicator - BE Feb /2/216 IFO Germany Feb /2/216 INSEE - FR Feb /2/216 Service PMI (flash) - EMU Feb /2/216 PMI manuf (flash) - EMU Feb /2/216 PMI manuf (flash) - DE Feb /2/216 Consumer confidence (flash) - EMU Feb /2/216 Industrial production %MM - EMU Dec /2/216 Industrial production %MM - FR Dec /2/216 Industrial production %MM - DE Dec Surprise indicator for EMU inflation GDP 21 (rhs) Indicator Date US activity Period Wgt % Cons. Actual Surprise* 4//216 Non-farm payrolls in k Feb //216 Construction spending %MM Jan //216 ISM manufacturing Feb /2/216 Pending home sales %MM Jan /2/216 Chicago PMI Feb /2/216 GDP (1st) %QQ Dec /2/216 Durable goods orders, ex transport %MM Jan /2/216 New home sales %MM Jan /2/216 Existing home sales %MM Jan /2/216 Consumer confidence Feb /2/216 Industrial production %MM Jan /2/216 Housing starts in k Jan /2/216 Unemployment rate Jan /2/216 Non-farm payrolls in k Jan /2/216 ISM non-manufacturing Jan Surprise indicator for US inflation By comparing the actual reading of an indicator with the corresponding consensus forecast, we calculate a surprise factor: Actuali Forecast i Surprise F actori Weighti StDev(Actuali Forecast i ) Thus, in order to calculate the surprise factor, the forecast error is standardised and weighted based on the importance we attach to the indicator. To calculate the standard deviation of the forecast error, the full available history of actual figures and consensus forecasts is used. Building on the surprise factor described in the box above, we calculate surprise indicators for economic activity and inflation in both the euro area and the US. The starting point of the indicator can be set to an arbitrary level and does not have any particular significance. The change in the indicator on any given day is given by the sum of the surprise factors of the indicators published on that day. Note that only the publication date of an indicator is relevant, not its reference period. If no data is released on a given day then the surprise factor for that day is zero. The activity surprise indicator for the euro area comprises 8 individual indicators, its counterpart for the US, 27. The inflation surprise indicator comprises 12 individual indicators for both the euro area and the US. CPI 21 (rhs) Indicator CPI 21 (rhs) Indicator Top 8 EMU surprises of the last four weeks ** Top 8 US surprises of the last four weeks ** Date EMU inflation Period Wgt % Cons. Actual Surprise* Date US inflation Period Wgt % Cons. Actual Surprise* 29/2/216 HICP (prelim) %MM - IT Feb /2/216 HICP (core, flash) %YY - EMU Feb /2/216 HICP (flash) %YY - EMU Feb /2/216 CPI (prelim) %MM - DE Feb /2/216 HICP (flash) %YY - ES Feb /2/216 HICP (prelim) %MM - FR Feb /2/216 HICP (final) %YY - EMU Jan /2/216 HICP (prelim) %MM - FR Jan Source: Bloomberg, Consensus Economics, Commerzbank Research 4//216 Average earnings %MM Feb //216 ISM input prices Feb /2/216 PCE (headline) %YY Jan /2/216 PCE (core) %MM Jan /2/216 CPI (headline) %MM Jan /2/216 CPI (core) %MM Jan /2/216 PPI (headline) %MM Jan /2/216 PPI (core) %MM Jan * surprise = ± (actual - consensus) / standard deviation (actual - consensus) ** releases with the largest impact on the respective surprise indicator are highlighted in bold The level of the indicator is itself not meaningful, as the starting point of the series can be set to an arbitrary level. What matters is the changes in the level of the indicator. A rising indicator means that the macro indicator constituents of a given surprise indicator have tended to surprise to the upside, while a decreasing indicator means that they have generally surprised to the downside over the time period under consideration. Our charts also include consensus growth and inflation expectations provided by Consensus Economics. In the case of the surprise indicators for economic activity, for example, it can be seen that an upward trend in the surprise indicator was followed by upward revisions in growth forecasts in both the euro area and the US. The tables show the data releases with the largest impact on the respective surprise indicator over the past four weeks. For data releases that make it into this table, at least one of the following two criteria will tend to be true: 1) the actual reading deviates considerably from the consensus forecast; and 2) the indicator has been assigned a large weight. The weights used in aggregating the different indicators have been set by our economists. They are based on the indicators relevance for the economic outlook. For instance, an indicator that is published early or that pertains to a large country will tend to have a larger weight. 11 May

16 How to read the FX Monitor The FX Monitor contains information on selected global foreign exchange rates, related interest rate differentials, carry trades and fx option implied volatilities. Focusing on the euro and US dollar, it summarises developments in DM and EM spot rates, carry trade performance in DM currencies and interest rate differentials. In addition, statistics based on fx implied volatility provide an insight to current market sentiment vs. the euro. FX Monitor Continued weak data in the eurozone, the run-up to the ECB meeting and better than expected activity and labour market data in the US did not prevent the USD from slipping vs the EUR on weaker than expected US wage inflation on Friday. The EUR/USD closed the week.65% up at 1.15, while hitting a low of on Wednesday. The market seems to believe the Fed is unlikely to raise interest rates unless the framework conditions are absolutely perfect. GBP weakness seems to have come to an end with GBP gaining each day vs the USD last week and closing the week 2.55% higher. GBP also gained 1.89% vs the euro. Positioning remains for further GBP weakness, suggesting that GBP has a chance of further stabilising. Sustainable upside though is only likely once the Brexit risk is removed. AUD, NZD and CAD gained almost each day last week and rose by up to 4% on the continued stabilisation of the oil price. Also all EM currencies appreciated last week with RUB, BRL and ZAR gaining more than 5%. Foreign exchange rates, related interest rates and fx derivative statistics Trade-weighted currency development /1/215 = 1 8 Mar-15 Jun-15 Sep-15 Dec-15 Mar-16 USD EUR JPY EM Currency moves vs USD and EUR (%) EUR JPY GBP CAD AUD NZD CHF NOK SEK DKK CNY INR IDR MYR SGD KRW TWD THB CZK PLN HUF RUB TRY ZAR BRL CLP COP MXN PEN EUR/USD and interest rate differential (IRD, %) G1 Asia Pacific EMEA LatAm 1W vs USD 1W vs EUR YTD vs USD (rhs) YTD vs EUR (rhs) 1.2 Jul-16 EUR/USD 1-year IRD (inv, rhs) CBK forecast CBK forecast (inv, rhs) Spot rates and IRDs Ccy Price Central bank rates 1Y IRD 1 as at 4//216 1W 1W 1W 1M YTD 12M Last 1M 12M Last 1M 12M Last low high change (%) (%) change (bp) (%) change (bp) vs EUR ECB rate.5 USD CAD GBP CHF SEK NOK JPY AUD NZD CNY RUB TRY PLN BRL ZAR vs USD Fed rate.5 25 CAD GBP CHF SEK NOK JPY AUD NZD CNY RUB TRY PLN BRL ZAR ¹current ten-year interest rate vs eurozone (proxied by German Bunds (.24%)) and USTs (1.87%). Calculated as (R(foreign ccy) - R(base ccy)) Carry perspective* 25-delta risk reversal (R/R): various option maturities (%) 1M 25-delta R/R: historical evolution (2D MA, %) W ago (blank), 2D MA (lines) 1W 2W W 1M 2M M 6M 1Y EUR/GBP EUR/USD EUR/CHF EUR/JPY Source: Bloomberg, Commerzbank Research *top vs bottom of largest developed markets in terms of M deposit rate, daily, since 1/1/29 We present year-to-date and oneweek changes vs. the US dollar and euro for G1 currencies and 19 of the most important EM currencies by EM region. The focus of the chart is on changes vs. US dollar, with the one-week change shown as an orange bar and the year-to-date change as a black diamond. Changes vs. the euro are shown as a grey line or grey diamond NZ 1st: NZ vs CH 2nd: AU vs SE 2.91 rd: NO vs DK Avg (1st to rd) CH SE DK EU JP UK CA US NO AU M deposit rates (%) -4 daily data (dashed) -5 EUR/GBP EUR/USD EUR/CHF EUR/JPY This chart shows the evolution of EUR/USD over the past week and puts this in relation to historical developments as well as CB forecasts. Additionally, the 1Y interest rate differential (IRD) is provided, calculated as the US 1Y bond yield less the German 1Y bond yield ( US DE r1 Y r ). 1Y The top-left chart illustrates the indexed development of the trade-weighted (effective) USD, EUR, JPY and EM exchange rates over the last year. Tradeweighted effective exchange rate indices are multilateral exchange rate indices compiled as a weighted average of exchange rates of home vs. foreign currencies, with the weight for each foreign country equal to its share in trade. The interpretation is that if the index rises, all else being equal, the purchasing power of that currency also rises. That would reduce the cost of imports but would undermine the competitiveness of exports. To account for effects of relative inflation rates, often a real effective exchange rate index is compiled as the product of the trade-weighted exchange rate index and the relative price index between the home economy and the trading partners. This table shows selected exchange rates (currency per one euro or US dollar) and related interest rate data. Using prices as reported at last week s closing, we present the change over the previous week, month, 12-months and year-to-date, as well as the previous week s low and high. In the middle block, central bank rates and their 1M and 12M changes are reported. The rates and trends indicate central banks current position in the rate cycle, and allow a direct comparison to the relevant change in fx rates vs. the euro and dollar. In the rightmost three columns, the 1Y IRDs and 1M as well as 12M changes are shown (for calculation details see call-out above). The values subtracted from each country s yield level to form the interest rate differential are given in the footnote (German 1Y yield or US 1Y yield respectively). From changes in the IRD one can form an expectation of changes in the exchange rate. An important driver short to medium term should be money flows from expected carry profits. Assuming a constant risk premium, in the long run a rising IRD should in theory result in depreciation of the variable currency. However, this hypothesis finds little support from data. It is commonly known as the forward puzzle May 216

17 How to read the FX Monitor (cont d) inv, rhs) Carry perspective* 14 M deposit rates (%) 1 CH -.84 SE DK EU JP -.8 UK.58 9 CA.78 8 US.79 7 NO.9 AU st: NZ vs CH 2nd: AU vs SE NZ 2.91 rd: NO vs DK Avg (1st to rd) Delta risk reversals (R/R) are formed by comparing implied volatilities of call and put options with the same absolute delta (sensitivity to the underlying s price). Here we look at 25-delta R/R, indicating that options with a delta of ± 25% are compared. The difference in implied volatility (and hence price) indicates whether market participants pay more for calls (R/R>) or puts (R/R<) (see sketch on the right). Due to systematic factors such as the safehaven effect, the average R/R is seldom zero. Thus, when we report R/R vs. euro, we include 2-day moving average levels for comparison. The short-term change (1W), is an indicator of trends as well, and can be identified by comparing current values to last week s levels (open markers in the chart). The right chart below plots historical R/R levels (and a 2-day moving average) for the specific 1M option maturity. Carry trades belong to the most basic speculative strategies in the fx space. In short, the investor tries to profit by borrowing money in one currency at a low rate and depositing it in a high yield currency over the same time horizon. Obviously, the strategy is not arbitrage-free since its performance depends on changes in the fx rate. The indices presented are based on carry trades within DM, as they are typically considered less risky. The current M deposit rates of the markets covered are provided in the chart on the right. Returns from carry trades are basically the sum of the change in the fx rate and the interest rate differential. In the indices shown here, a one-day investment horizon is assumed. Each trading day, the top and bottom three currencies in terms of M deposit rates are identified and the best and worst yielding currencies are matched, as well as the second and third best/worst pair. The time series shown represent cumulative returns with base date 1 January 29 for first, second and third pair. Note that the corresponding currency combinations may vary over time, e.g. the cheapest lending could be possible in Japanese yen on one day and Swiss francs on the next day, implying that the first pair consists partly of yen on one day, changing to the Swiss franc for the next day. FX volatility skew (below) Risk reversals and butterflies quantify key properties of the fx option implied volatility skew Implied Volatility (%) = R/R Fly ± delta delta of OTM calls delta of OTM puts at strike 25-delta risk reversal (R/R): various option maturities (%) 1M 25-delta R/R: historical evolution (2D MA, %) W ago (blank), 2D MA (lines) 1W 2W W 1M 2M M 6M 1Y EUR/GBP EUR/USD EUR/CHF EUR/JPY daily data (dashed) -5 Jan-1 Jul-1 EUR/GBP Jan-14 Jul-14 EUR/USD Jan-15 Jul-15 EUR/CHF Jan-16 EUR/JPY It is essential to be aware of foreign exchange conventions when dealing with currency quotes. The exchange rate between two currencies is defined as standard units of the variable (or quote, price, payment) currency available for one unit of base (or unit, transaction) currency. By convention, the preferred base currency is (in this order) euro (EUR), sterling (GBP), aussie (AUD), kiwi (NZD), dollar (USD). Note that not all classical commodity currencies are based on the USD. The euro-dollar rate (number of dollars per one euro) is labelled EUR/USD or EUR-USD. Thus the / -sign cannot be interpreted as a fraction stroke. If the variable currency appreciates, the fx rate decreases. The reverse relation holds between the base currency and a change in the fx rate. We use the so-called indirect quotation, which is common in the eurozone and UK, and defines the home country s currency as the base currency. When we report changes vs. the euro, they reflect a gain or loss of the counterpart s value. 11 May

18 How to read the Central Bank & Inflation Monitor The Central Bank & Inflation Monitor contains information on central bank rates in three major economic regions: the eurozone, the US and the UK, including market-implied probabilities for a change in the major rate at one of the next meetings. It presents data on the corresponding money market rates and recent and longer-term developments in market- and surveybased inflation expectations. Central Bank & Inflation Monitor During her semi-annual Congressional testimony, Janet Yellen indicated that recent developments among others the financial market turmoil may have changed the Fed s risk assessment. Markets have for long priced that, with Fed funds futures currently pricing a %-probability for a rate hike at the next meeting in March. The Riksbank joined the rate cut bandwagon, lowering its key rate by 15bp to -.5% after cutting its inflation projections. Eurozone 5Y5Y inflation swaps fell below the January 215 levels after final inflation data from Spain and Germany was unchanged vs the preliminary release, but Dutch data disappointed. The University of Michigan s survey about median US inflation over the next five to ten years fell to 2.4%, the lowest on record. Money market rates in the eurozone continued to decline, with most of them reaching their weekly low on Thursday. Central banks, money markets and inflation ECB, Fed, BoE (%)* Switch from refi rate to depo rate as major policy rate Central bank rate 1M EONIA/OIS/SONIA swap Money market rates Market-implied probability for next central bank move (%) 5Y5Y inflation swap rate (%) Break even inflation rates (5Y) (%)** Inflation swap curves (zero vs inflation) (%)*** Source: Bloomberg, Commerzbank Research 2 ECB: next decision on 1// FOMC: next decision on 16// BoE: next decision on 17// CBK central bank rate forecasts 1M EONIA/OIS/SONIA fwd 1.2 EUR 5Y5Y inflation swap rate US 5Y5Y inflation swap rate EZ infl. in 5 years (ECB SPF, rhs) Avg. 1Y US infl. (Fed SPF, rhs) US (CPI) UK (RPI) FR (CPI) EU (HICPx) as at 4//216 EUR 1W chg YTD UK 1W chg YTD US 1W chg YTD (%) (bp) (bp) (%) (bp) (bp) (%) (bp) (bp) EONIA / SONIA / OIS 1D W Swap 1M Swap M Swap M Swap M Swap Euribor / GB Libor / US Libor 1W M M M M Eurepo / GB Repo (RONIA) / US-Sov Repo O/N Mar 27-Apr 15-Jun 27-Jul 21-Sep 2-Nov 14-Dec US UK DE (HICPx) Market implied inflation and real yields *black diamonds show next CB meetings; ** =start, = end of interpolation period; ***dashed line: 1W ago 1-Feb 1-Mar 21-Apr 2-Jun 21-Jul 8-Sep Fed (25bp) ECB (1bp) BoE (25bp) Hike Cut Probability 1W ago Probability for second move -.5 as at 4//216 EUR 1W chg YTD UK 1W chg YTD US 1W chg YTD (%) (bp) (bp) (%) (bp) (bp) (%) (bp) (bp) Zero coupon inflation swaps 1Y Y Y Y Y Break-even inflation (nominal versus inflation-linked) 2Y Y 1) Y 1) Y Yield inflation-linked bonds 5Y 1) Y 1) Oct 1) may be based on interpolated values for DE (see chart above), which is used as proxy for eurozone This chart shows the current shape of the zero coupon inflation swaps (ZCISs) yield curve, as well as its shape one week ago. A ZCIS is an agreement between two parties, the inflation receiver and the inflation payer, to exchange payments on maturity. The inflation receiver pays the fixed leg for a given nominal N and fixed rate r and receives the inflation leg on maturity (in T years). The inflation payer receives the fixed and pays the inflation leg: T Inflation leg IT / I ) N, Fixed leg ( 1 r) N ( I denotes the underlying inflation index, i.e. the urban CPI in the US, the RPI in the UK, the HICPxT in the eurozone and the CPIxT in France. 8-Dec 2-Jan 17-Mar 14-Apr 12-May 16-Jun 14-Jul 4-Aug 15-Sep 1-Oct EONIA (Euro Overnight Index Average) is an effective overnight rate, computed by the ECB as a weighted average of all overnight unsecured lending transactions in the interbank market. SONIA and OIS are equivalent overnight index rates in the UK and in the US. Overnight index swaps exchange the ongoing daily overnight index rates for the respective swap rate. Euribor (Euro Interbank Offered Rate) is the benchmark rate of the euro money market. A sample of leading banks provides daily quotes. Euribor is calculated as the average of these rates after elimination of the 15% highest and lowest quotations. A similar procedure is applied to arrive at UK Libor and US Libor. The lower part of table shows current repo rates, defined as the effective rates at which central banks repurchase government securities from commercial banks after an agreed time. Derivative instruments based on money market rates like Fed funds futures can be used to determine the marketimplied probability for a change in the underlying central bank rate. The columns of the chart show the marketimplied probability of one rate change, i.e. the probability that the central bank rate will be changed from the current level by the amount denoted in the legend at the specified meeting. The cross denotes the probability of a second move or a move twice as large. This table contains key figures on the inflation market. The upper part shows the ZCIS rates in the eurozone, the US and the UK. Below these, current break even inflation rates are stated, defined as the difference between the nominal yield of a government bond and the real yield of a maturity-matched inflation-linked government bond. It is considered a proxy of the market s inflation expectations: if the subsequently realised inflation is higher (lower) than the break even rate, the inflation linked bond would outperform (underperform) its nominal counterpart. The lower part of the table shows the real yield of inflation-linked bonds. Details are provided on the next page May 216

19 futures currently pricing a %-probability for a rate hike at the next me How to read the Central Bank & Inflation Monitor (cont d) Central Bank & Inflation Monitor (%) (bp) (bp) (%) (bp) (bp) (%) (bp) (bp) 11 May The Riksbank joined the rate cut bandwagon, lowering its key rate by Eurozone 5Y5Y inflation swaps fell below the January 215 levels a unchanged vs the preliminary release, but Dutch data disappointed. inflation over the next five to ten years fell to 2.4%, the lowest on reco Money market rates in the eurozone continued to decline, with most o Central banks, money markets and inflation ECB, Fed, BoE (%)* Money as at 4//2 During her semi-annual Congressional testimony, Janet Yellen indicated that recent developments among others the This chart shows time series of the main interest rates of 6 2 ECB: next financial market turmoil may have changed the Fed s risk assessment. Markets have for long priced that, with Fed funds decision on EONIA / S the 1//16 1D futures ECB, currently the Fed pricing and a %-probability the BoE (yellow) for a rate and hike at the the next meeting in March. 1 1W corresponding Commerzbank forecast (dashed). Black 1M The Riksbank joined the rate cut bandwagon, lowering its key rate by 15bp to -.5% after cutting its inflation projections. M markers indicate dates of policy decisions. Next to these 6M Eurozone 5Y5Y inflation swaps fell below the January 215 levels after final Switch inflation from refi data rate to from depo Spain and Germany was 12M time series, the corresponding Overnight Index Swap rate as major policy rate unchanged vs the preliminary release, but Dutch data disappointed. The -University of Michigan s survey Euribor / G -1about median US 1W (OIS) inflation data as over well the as next related five to market ten years expectations fell to 2.4%, (forward the lowest on record. 1M 1M) are plotted. The main refinancing rate is the ECB s M Money market rates in the eurozone continued to decline, with most of them 6 reaching their weekly low on Thursday. 2 FOMC: next 6M decision on primary means of providing liquidity to the banking sector 12M 16//16 Eurepo / G in Central the eurozone banks, money through markets Main and Refinancing inflation Operations. O/N 1 During ECB, Fed, periods BoE (%)* of high excess liquidity, e.g. due to Money market rates Market as at 4//216 EUR 1W chg YTD UK 1W chg YTD US 1W chg YTD unconventional measures, the deposit facility rate 6 2 ECB: next (%) (bp) (bp) (%) (bp) (bp) (%) (bp) (bp) 1 decision on EONIA / SONIA / OIS becomes the more important monetary policy 1//16 rate for 1D W Swap markets, and hence the more important rate for monetary 1M Swap BoE: next 6 M Swap policy setting. Central In the US, Bank the Fed & sets Inflation the Federal Monitor funds decision on 6M Swap Switch from refi rate to depo 17//16 12M Swap target rate, as major which policy rate is the desired interest rate for Fed - Euribor / GB Libor / US Libor -1 2 During her semi-annual Congressional testimony, Janet 1 1W Yellen -.27 indicated that recent developments 1..4.among.8 others the funds (overnight reserves). In the past, the New York 1M financial market turmoil may have changed the Fed s Mrisk assessment Markets have -.2 for long -.2 priced that, 2.1 with Fed funds Reserve 6 Bank managed the 2 Federal FOMC: next funds rate by 6M futures currently pricing a decision %-probability on for a rate hike at the next meeting in March. 12M undertaking repos and reverse repos 16//16in the market, but Eurepo / GB Repo 2 (RONIA) 2/ US-Sov 26 Repo The Riksbank joined the rate cut bandwagon, lowering O/N its key rate -.29 by 15bp Central.2 to bank.8-.5% rate.48 after. cutting.6 its.4inflation CBK 15.5 central projections. bank rate forecasts the high excess liquidity 1resulting from the Fed s 1M EONIA/OIS/SONIA swap 1M EONIA/OIS/SONIA fwd Hik quantitative Eurozone easing made 5Y5Y it inflation necessary swaps to fell pay below interest the January on Market-implied 215 levels probability after final inflation for next data central from bank Spain move and (%) Germany was unchanged vs the preliminary release, but Dutch data 1disappointed. 5Y5Y inflation The University swap rate of Michigan s (%) survey about median US Break e excess reserves and to offer deposits (overnight reverse inflation over the next five to ten years fell to 2.4%, the lowest on record. repos) to institutions not allowed to deposit with the Fed to keep the rate Money within market the desired rates 2 in the range. BoE: eurozone next The Bank continued rate to is decline, 6 with most of them reaching their weekly low on Thursday. decision on the rate of interest that the Bank 17//16 Central banks, money markets of and England inflation pays on reserve balances held by commercial banks and building ECB, Fed, BoE (%)* Money 2. market rates societies. This chart contains information on marketimplied inflation for 5 years in 5 years time, as.5 as at 4//216 EUR 1W chg YTD UK 1W chg YTD US 1W chg YTD ECB: next (%) (bp) (bp) (%) (bp) (bp) (%) (bp) (bp) decision on EONIA / SONIA / OIS Fed (25bp) ECB (1bp) BoE (25bp). Central bank rate CBK central bank rate forecasts 1//16 1Dderived from -.24 the.7 inflation swap -.2curve Also.7 shown M EONIA/OIS/SONIA swap 1M EONIA/OIS/SONIA fwd 1.21W Swap Hike Cut Probability 1W ago Probability for second move Jan-1 1Mis Swap the Jul-1 -.ECB s Jan survey Jul Jan-15.1 of 1.2professional Jul-15.8 Jan M Swap EUR 5Y5Y -.5 inflation -.5 swap rate.46.2us 5Y5Y swap.2 rate 2.9 Jan-1 5Y5Y inflation swap rate (%) Break even inflation rates (5Y) (%)** 6Mforecasters Swap EZ infl. in years (SPF) (ECB SPF, for rhs) longer-term.45.4 Avg Y inflation US.45 infl. (Fed(four SPF,.8 rhs) Switch from refi rate to depo M Swap rate as major policy rate. - Inflation Euribor or / five GB swap Libor calendar / US curves Libor years (zero ahead vs inflation) Q1, (%)*** Q2 or Q, Q4-1 Market W as at 4// Mrespectively) The ECB.51 monitors..5 these.44inflation M FOMC: next 2. 6Mexpectation -.14 indicators, as the.5 first -1.4 reflects the 4.6 Zero coup decision on 1Y M //16 market s medium to long-term inflation Eurepo / GB Repo (RONIA) / US-Sov Repo 2Y O/N Y expectations, while the second shows the view of 1Y Market-implied professional probability forecasters. next Both central are based bank on move the (%) 2Y Break-eve Y harmonised index of consumer prices. For the Y 1) EUR 5Y5Y inflation swap rate US 5Y5Y inflation swap rate Jan-1 Jul-1 8 US, Jan-14 we show Jul-14 the Philadelphia Jan-15 Jul-15 Fed s Jan-16 SPF. As 1Y. 1) EZ infl. in 5 years (ECB SPF, rhs) Avg. 1Y US infl. (Fed SPF, rhs) US UK DE (HICPx) 2Y 6 2 BoE: next opposed to the ECB s SPF, forecasters are asked Yield inflat Inflation swap curves (zero vs inflation) (%)*** decision on Market implied 4 inflation 2 4 and 6 8real 1yields Y 1) 17//16 to provide an estimate for annual average as at 4//216 US (CPI) UK (RPI) FR (CPI) EU (HICPx) 1Y.5 EUR 1W chg YTD UK 1W chg YTD US 1W chg YTD 1) 2 1 inflation (%) (bp) over (bp) the (%) next (bp) ten (bp) years. (%) (bp) (bp). Zero coupon inflation swaps 1Y Source:.1 Bloomberg,.5-9.8Commerzbank Research *black 15.2 diamonds show next CB meeting This 2.5 chart shows generic breakeven inflation rates for 2Y Y average inflation over five years, the most liquid maturity 1Y Y bucket. Periods Central without bank rate a suitable bond CBK are central linearly bank rate forecasts Fed (25bp) ECB (1bp) BoE (25bp) 1. Break-even inflation (nominal versus inflation-linked) 1M EONIA/OIS/SONIA swap 1M EONIA/OIS/SONIA fwd 2Y Hike Cut Probability 1W ago 1.24 Probability for second move interpolated.5 between the next available maturity buckets. 5Y 1) Y5Y inflation swap rate (%) 1Y Break. 1).85 even 11. inflation rates.5 (5Y) -7.2 (%)** The interpolation period is indicated in the chart via the 2Y Yield markers (beginning of period) and (end of period). inflation-linked bonds Y 1) The underlying 2.8 US (CPI) inflation-linked UK (RPI) bonds FR (CPI) (linkers) EU (HICPx) pay a 1Y ) ) may 2.5 be based on interpolated values for DE (see chart above), which is used as proxy for eurozone 2.6 coupon which is adjusted for the change in the (typically 2.15 Source: Bloomberg, 2.4 Commerzbank Research *black diamonds show next CB meetings; ** 2.=start, = end of interpolation period; ***dashed line: 1W ago two months 2.2 lagged) benchmark inflation index. Issues usually feature a deflation protection mechanism However, 1.6 breakeven rates provide biased inflation expectation estimates, since they also reflect a liquidity premium for linkers, an inflation risk premium and EUR 5Y5Y inflation swap rate US 5Y5Y inflation swap rate seasonality patterns. EZ infl. in 5 years (ECB SPF, rhs) Avg. 1Y US infl. (Fed SPF, rhs) US UK DE (HICPx) Inflation swap curves (zero vs inflation) (%)*** Market implied inflation and real yields Mar 27-Apr 15-Jun 27-Jul 21-Sep 2-Nov 14-Dec 1-Feb 1-Mar 21-Apr 2-Jun 21-Jul 8-Sep 2-Oct 8-Dec 2-Jan 16-Mar 27-Apr 15-Jun 27-Jul 21-Sep 2-Nov 14-Dec 1-Feb 17-Mar 14-Apr 12-May 16-Jun 14-Jul 4-Aug 15-Sep 1-Oct 1-Mar 21-Apr 2-Jun 21-Jul 8-Sep 2-Oct 8-Dec 2-Jan 17-Mar 14-Apr 12-May 16-Jun 14-Jul 4-Aug 15-Sep 1-Oct.5 as at 4//216 EUR 1W chg YTD UK 1W chg YTD US 1W chg YTD Zero coupon inflation swaps 1Y Y Y Y Y Mar

20 How to read the Sovereign Monitor The Sovereign Monitor contains information on debt issued by national governments. The yield of a specific sovereign bond is mainly determined by factors relating to the issuing country, for example, creditworthiness, economic strength, monetary and fiscal policy, and inflation expectations. Additionallly, the specific coupon and maturity will effect the yield, as well as supply and demand in the market. Sovereign Monitor Sovereign safe-haven debt continues to defy expectations as despite the stabilisation of oil and risk assets, inflation expectations continue to deteriorate pushing yields ever lower. Ten-year JGB yields are now at all-time lows at.7%, Gilts and US Treasuries remain close to January 215 levels, the lowest since 212. Bund yields also fell hitting.14%, the lowest levels seen since the all-time low of April 215. The periphery mostly outperformed the core, PGBs and GGBs fell 4bp and 25bp respectively, BTPs and SPGBs dropped 7.6bp and 12bp while IRISH slightly underperformed held back by the joint uncertainties of their parliamentary elections and the UKs Brexit referendum. IRISH are likely to continue to underperform after the inconclusive election result. This week we expect to see c. 18bn supply from Germany, France and Spain while the window is open for Fitch to review Portugal s rating (BB+ pos). In light of its new fiscal policy course, the positive outlook is clearly at risk. Yields & CDS Government bond vs swap 4//216 Govt -1W YTD Swap Govt -1W YTD Swap (%) (bp) (bp) (%) (%) (bp) (bp) (%) DE 1Y UK 1Y Y Y Y Y Y Y Y Y Y Y US 1Y Japan 1Y Y Y Y Y Y Y Y Y Y Y Change in nominal 1-year yield breakdown (bp) 1 week 1 month YTD Nominal yields (1Y) (%) Yield curve steepness (1Y - 2Y) (%) Eurozone spread development vs Bunds (bp) Eurozone performance total return (%) 7 DE US UK DE US UK Inflation DE US UK Real DE US UK Nominal. DE US UK 4 2 The bond vs. swaps table highlights the yield difference between a point on a government bond curve and the equivalent point on the appropriate plain vanilla interest rate swap curve. The swap curve is an important benchmark in the fixed income market. Typically, the swap curve has a similar shape to the government bond curve. In most cases, however, swap rates will be higher than government bond yields, reflecting the greater counterparty risk. The yield difference between the curves is known as the swap spread. For more details on swaps, please see the next page. The breakdown of the change in 1- year yields shows how much of a nominal yield movement is due to a change in real yields and how much is due to a change in implied inflation. Implied inflation is the future inflation rate as implied from inflation linked bonds, and is a measure of current market inflation expectations Italy Spain Ireland Portugal France Yield differential US - DE (%) Y Source: Bloomberg, Markit, Commerzbank Research 2Y GR PT ES IT IE FI NL FR DE BE AT 1-week 4-week YTD (rhs) Sovereign CDS 5Y (bp) as at 4//216 Spot -1W YTD Spot -1W YTD Western Germany Eastern Russia Europe UK Europe Bulgaria Switzerland 2 1 Romania France 4-8 Hungary Italy Poland Ireland Croatia Greece America US 19 2 Spain Brazil Portugal Mexico Belgium Venezuela Sweden 17 4 Asia Japan 47 Norway 1 China Other South Africa South Korea Australia Thailand The 1Y yield is a pivotal benchmark for government bond markets. It is often used as a reference yield for the entire government yield curve in order to convey information about the general yield level in a specific currency zone. This chart shows eurozone total returns for 1-week, 4-week and year-to-date periods. The ten-year yield difference vs. Bunds shows the historical development of eurozone peripheral spreads. An increase in the spread shows a worsening of the credit risk for the respective country. The spread tends to increase during periods of risk off. A CDS for a sovereign issuer is a credit derivative contract between two counterparties. The buyer pays a periodic amount to the seller. In return, the buyer will receive a payment should the underlying sovereign default or restructure its debt. Usually, the amount that the buyer pays is expressed in basis points of the bond nominal. This figure is known as the CDS spread. Sometimes, CDS contracts are compared with insurance policies, acknowledging the risk hedging character of this type of derivative. However, there are differences between CDS contracts and insurance policies. The main difference is that the buyer of a CDS contract does not need to own an underlying government bond. In this case, the CDS contract can be used to speculate on sovereign debt by an investor who is said to be naked May 216

21 How to read the Sovereign Monitor (cont d) Jan-16 Yield curve steepness (1Y - 2Y) (%) EU US UK An interest rate swap is an agreement between two counterparties to exchange specific interest payments. These swaps are interest rate derivatives, which trade over the counter. The most commonly traded and most liquid interest rate swaps are known as plain vanilla swaps. These exchange a fixed-rate payment for a variable payment, which is usually based on LIBOR (a short-term reference rate of interest in the interbank market). The party Nominal receiving yield curves the fixed (%) rate is the receiver and the. other party the payer. The fixed interest rate received 2.5 is the swap rate. From a valuation 2. perspective, interest rate swaps are priced to 1.5 ensure a 1.zero net present value at initiation. Under this condition,.5 the swap rate corresponds to the. forward LIBOR curve. As time passes, an interest -.5 rate swap s -1. value can become positive or negative for the receiver or payer. Interest rate swaps can be used to modify the interest rate sensitivity of a bond portfolio by increasing or decreasing the portfolio s net duration DE US UK JP 1W ago 1W ago 1W ago 1W ago The steepness of a yield curve is defined here as the difference between the ten-year and the two-year bond yields for a particular issuer. The steepness reflects the shape of the underlying yield curve. Generally, there are three primary shapes for term structures. For a normal yield curve, the short-term yield is lower than the long-term yield. If the opposite is true, the yield curve is then said to be inverted. A term structure with equal yields across all maturities is referred to as a flat yield curve. A normal yield curve indicates that investors require a higher rate of return for long-term debt due to the additional risk associated with the longer time to maturity. Pronounced steepness of the yield curve is often associated with investors expecting that the economy will face strong future growth and higher future inflation. Conversely, an inverted yield curve suggests the expectation of a contraction in the economy and lower rates of inflation. Government bond vs swap 4//216 Govt -1W YTD Swap Govt -1W YTD Swap (%) (bp) (bp) (%) (%) (bp) (bp) (%) EU(DE) 1Y UK 1Y Y Y Y Y Y Y Y Y Y Y US 1Y Japan 1Y Y Y Y Y Y Y Y Y Y Y Yield differential US - EU (%) Y 2Y The yield difference between US Treasuries and euro sovereign debt is an important measure for those concerned with the future development of the exchange rate of EUR vs. USD. Empirical analyses show that a rising interest rate differential (US-EUR) Change in nominal tends 1-year to lead yield to an breakdown appreciation of the USD vs. EUR. 1 week This is, at first 1 month glance, at odds YTD with the interest rate parity theorem. One reason for this could be the fact that a large 1 difference between the EUR and US yield triggers international investors -2 to shift money into the 4-25 higher yielding asset, resulting in a greater demand in the higher -1-7yielding currency and more supply in the lower yielding currency EU* US UK Inflation -2 EU* US UK Real -5 EU* US UK Nominal 11 May

22 How to read the Equity Monitor The Equity Monitor gives an overview of key equity markets around the world. It contains a fundamental analysis of DM and EM, focusing on valuation, earnings growth, earnings revisions, earnings uncertainty and information on either the recent or forthcoming US reporting season. This table changes over time but generally contains information on the current or forthcoming US earnings reporting season. During the reporting season it shows details for the S&P 5 or Stoxx 6 companies which have already published earnings and revenues. The surprise factor is defined as the gap between realised earnings and forecast figures. Ahead of the reporting season either expected earnings & revenue growth figures or preannouncement statistics are shown. Equity Monitor The equity sell-off continued last week as global equities declined (down 2.5%) due to ongoing growth concerns and intensifying worries about the robustness of the European banking sector. However, DM equites stocks (down 2.4%) fared markedly better than EM equities (down.8%) as US equities only slightly retreated (down.7%). US Q4 reporting remains solid, with 68% of the 81 companies which have so far posted results beating earnings expectations. While nearly all the Financials and Industrials companies have posted Q4 results already, there are still many companies yet to report in the Consumer Discretionary, Consumer Staples, and Energy sectors which should give an indication if US recession fears are exaggerated as we assume. Q4 reporting in Europe remains lacklustre, the earnings beat rate declined further from 47% in the previous week to 44% last week as 18 companies have so far posted Q4 results. In particular health care companies disappointed so far. Market overview Total returns: equity style and size (%) STOXX TMI Growth Russell Growth STOXX TMI Value Russell Value STOXX Small 2 STOXX Mid 2 Russell Small Cap Russell Mid Cap STOXX Large 2 Russell Large Cap EUR YTD EUR 1W (top) 12M fwd P/E ratio developed markets Balance of earnings estimate revisions*** S&P 5 Stoxx 6 Equity markets: valuation, earnings, growth and revisions 14. Source: Datastream, Thomson Reuters, Commerzbank Research; 15. S&P 5 Avg S&P 5 Stoxx 6 Avg Stoxx Topix Earnings revisons -month rolling Index MSCI EM Asia MSCI EMEA MSCI EM LatAm Average Valuation S&P 5: Q4 215 reporting season As at 4//216 Sector Earnings (reported 494) Revenues (reported 492) Beat % In-line % Fwd earnings: uncertainty** & development rel. to trend *12M fwd P/E, **standard deviation as % of earnings, ***(up rev - down rev) / total companies Miss % Surpr. % Beat % In-line % Miss % Surpr. % Index Total # Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Materials Technology Telecom Services Utilities S&P Average (since 1994) Earnings uncertainty** 12M forward index earnings & trend earnings Earnings deviation from trend % S&P 5 Stoxx Earnings Earnings growth Revisions P/E Price P/B Div Un- Company rev Rev 4//216 yield certainty** (total/up/down) ratio*** 216E 217E 1Y avg* current 1/M chg (%) current 1/M chg (%) 216E 217E MSCI World / / (168/47/116) -.9 S&P / / (499/152/46) -.9 Stoxx / / (589/167/41) -.42 Euro Stoxx / / (5/8/42) -.68 DAX / / (29/7/22) -.52 MSCI UK / / (111/5/76) -.7 SMI / / (2/5/15) -.5 Topix / / (14/14/442) -.12 MSCI Pacific ex Japan / / (147/7/19) -.49 MSCI EM / / (825/217/574) -.4 MSCI EM Asia / / (544/14/9) -.48 MSCI EMEA / / (152/42/14) -.41 MSCI EM Latin America / / (12/9/76) -.1 The equity markets overview is divided into four areas: valuation, earnings, growth and revisions. The valuation section covers standard ratios such as price-to-book, dividend yield and price-to-earnings for the next two fiscal years. The earnings part contains, besides estimates for the next two years, the 1 and -month percentage change of these estimates, allowing the identification of regions where changes in expectations have been greatest. Earnings growth is detailed in the next columns, followed by a summary of the number of company revisions. The revision ratio represents the dynamic of earnings revisions. A positive number indicates that there are more upward than downward revisions since last month, and vice versa This chart shows the 1W and year-todate euro total-return performance of equity style and size indices for European and US markets. At the top of the chart, style indices (value/growth) are sorted by 1W performance; this is also the case for the size indices (small/mid/large) displayed at the bottom of the chart. The chart of the 12M forward priceearnings ratio for Europe and the US allows the comparison of current valuations with historical averages. The ratio is defined as market value per share divided by the estimated earnings per share for the next 12 months. In general, a high P/E suggests that investors are expecting higher earnings growth relative to companies with a lower P/E ratio. As each industry has very different growth prospects, it is usually more useful to compare the P/E ratio of an index to its own P/E history. The Forward earnings: Uncertainty and development relative to trend section details the volatility of earnings estimates for Europe and the US. Earnings uncertainty is calculated as the standard deviation of the forward earnings in % and trend earnings are computed via a log regression the independent variable is log price, which is time-dependent. Earnings deviation from trend is calculated as the quotient of forward to trend earnings, minus May 216

23 How to read the Equity Monitor (cont d) The earnings uncertainty is calculated from the standard deviation of analysts 12-month-forward company earnings estimates and then aggregated to the index level. Fwd earnings: uncertainty** & development rel. to trend Earnings uncertainty** The Institutional Brokers Estimate System (I/B/E/S) provides bottom-up earnings forecasts and related data for countries and major international indices rather than for individual stocks. The earnings expectations of all stocks in an index or country are taken to create weighted forecast earnings, growth rates and P/E estimates for each market. I/B/E/S Aggregates are not indexed to a base year. There are no price indices calculated for these aggregates, and the index calculations, for the most part, utilise weighted averaging. For example, the simple formula for the EPS calculation is: 1 I/B/E/S aggregate EPS = Weighted earnings for I/B/E/S companies / Shares outstanding for I/B/E/S companies M forward index earnings & trend earnings Earnings deviation from trend % All company level data is calendarised prior to aggregation. I/B/E/S follows the Compustat rule when sorting fiscal year data into calendar years: Data for fiscal years ending between January and May of the current calendar year is included in the aggregate for the prior calendar year. Data for fiscal years ending between June and December of the current calendar year is included in the current calendar year aggregate. -25 S&P 5 Stoxx estimate revisions*** month rolling The earnings deviation from trend illustrates the cyclical fluctuation around long-term midcycle earnings. The development obviously mirrors that of the economic cycle. Given the mean-reverting nature of the deviation from trend, this measure gives a good indication of the potential for further earnings upgrades or downgrades MSCI EM Asia MSCI EMEA MSCI EM LatAm Average Aggregates are generally calendarised to a calendar year ending in December. Japan s aggregates, however, are mapped to a calendar year ending in March, as the majority of the country s companies have fiscal years ending in March. The data for the month end overwrites all previous monthly values and adds the latest month s values. The monthly update is a snapshot taken on the Thursday before the rd Friday of every month at close of business in New York. Index earnings tend cyclically to fluctuate around a long-term constant growth trend. The average growth rate since 1997 is 6% in the US and 5% in Europe. The period covered under 12- month-forward is based on the current date. 12-month-forward data is calculated on a pro-rata basis, e.g. the 12M fwd EPS can be calculated as: M/12 * current FY EPS + (12 M)/12 * next FY EPS where M is the number of months in the current fiscal year (FY). Balances of earnings estimate revisions are displayed for DM (US, Europe and Japan), as well as for EM EMEA (Europe, Middle East, Africa), LatAm and Asia. The earnings estimate revisions ratio is defined as the difference between the number of companies with upward earnings revisions (12-month-forward mean) and that of companies with downward earnings revisions (both since last month), divided by the total number of companies in the index. To smooth this number, in practice we use the -month rolling average. 11 May 216 2

24 How to read the Credit Monitor The Credit Monitor contains information on debt issued by corporations, or corporate bonds. The yield of a corporate bond is mainly determined by the issuer s creditworthiness, the underlying swap curve, any assets or guarantees backing the debt, and the maturity of the bond. The creditworthiness of a corporation depends largely on its capital structure and the chosen business model. Credit Monitor Spread widening dominated across all corporate bond areas. While EUR investment grade held up considerably well last week as in particular higher-rated non-financials even saw spreads tighter, USD and GBP bonds as well as high yield corporates widened by double-digit numbers. CDS indices exposed a similar picture, though US IG CDSs did not widen as strongly as cash bonds. Best performing sectors last week were consumer services and technology which both tightened by 1bp, while oil & gas and financials saw the strongest widening of spreads. Financials strongly underperformed non-financials last week in particular in EUR credit as spreads widened by 12bp for financials and by only 2bp for non-financials. Last week, Moody s announced 1 upgrade and 24 downgrades in the US and 1 upgrade and 5 downgrades in Western Europe. Credit markets Asset swap spread (in bp) and performance development (iboxx, itraxx/cdx) as at 26/2/216 Asset swap spread 6d Yield Mdur Asset swap spread 6d Yield Mdur Asset swap spread 6d Yield Mdur TR (%) TR (%) TR (%) EUR 1W YTD Z* (%) 1W YTD USD 1W YTD Z* (%) 1W YTD GBP 1W YTD Z* (%) 1W YTD Non-Fin. All A-AAA BBB Financials All A-AAA BBB Sen Sub Corporate All IG HY Covered All Sub-Sovereigns All CDS IG HY Cash & CDS spreads investment grade (bp) 25 Cash & CDS spreads high yield (bp) 7 6 The level of credit risk associated with a corporate bond is approximated by the issue s asset swap spread (please see the next page for more details). The asset swap spread shows the additional yield that the issuer has to pay on top of the floating rate (e.g. Euribor), to compensate for its credit risk. The investor can lock in this spread by entering an asset swap deal which turns a fixed-coupon bond into a floating rate note and hedges part of his price risk. Besides the asset swap spread, we also display a 6-day Z-Score, current yield and modified duration, as well as performance figures for 1-week and year-to-date. The data is displayed in three blocks, the first block shows data on EUR-, the second for USD- and the third for GBPdenominated bonds EUR cash EUR CDS USD cash USD CDS Asset swap spread by sectors (investment grade) (bp) Banks 9 22 Fin. Services Insurance Basic Mat Cons. Goods Cons. Svc Healthcare Industrials Oil and Gas Technology Telecom Utilities EUR USD 1W YTD Asset swap spread differential financials non-fin. (bp) EUR USD GBP Source: Bloomberg, Markit, Commerzbank Research EUR cash EUR CDS USD cash USD CDS Asset swap spread by sectors ( high yield) (bp) Fin 455 Basic Mat 65 Cons. Goods 441 Cons. Svc. 65 Healthcare 455 Industrials 59 Oil & Gas 678 Technology 469 Telecom. 569 Utilities W YTD Rating actions up/down ratio (Moody's).8 QTD YTD.6 Up Down Up Down USD EUR Q2/7 Q2/8 Q2/9 Q2/1 Q2/11 Q2/12 Q2/1 Q2/14 Q2/15 US Western Europe (-1 = 1% Downgrades, +1 = 1% Upgrades) * 6-day Z-Score (no. of std. dev. from mean) The two tables in this row show how the asset swap spread levels of investment grade bonds (left) and high yield bonds (right) have changed over time. Data shown is for at least the last 2 years. Additionally, we compare cash bond asset swap spreads with CDS index levels in these charts (See next page for details). The sector breakdown shows how asset swap spreads have developed in individual sectors. Again, the two left charts show investment grade and the two right charts high yield data, while the larger chart shows spread levels and the smaller chart spread changes over the last week and year-to-date. Financials vs. non-financials: This topic is often cited when talking about corporate bonds. This chart displays the spread differential between financials and nonfinancials over time for EUR, GBP and USDdenominated bonds. If the differential increases, this generally indicates that financials have underperformed non-financials (as they have widened more than financials or tightened less) and vice versa. The chart of rating actions shows the development of the upgrade/downgrade ratio for Western Europe and the US. The ratio is defined as the difference between the number of upgrades and the number of downgrades, divided by the total number of actions. This means that the ratio is +1 if all actions are upgrades and -1 if all actions are downgrades May 216

25 How to read the Credit Monitor (cont d) Asset swap spread (in bp) and performance development (iboxx, itraxx/cdx) as at 26/2/216 Asset swap spread 6d Yield Mdur Asset swap spread 6d Yield Mdur Asset swap spread 6d Yield Mdur TR (%) TR (%) TR (%) EUR 1W YTD Z* (%) 1W YTD USD 1W YTD Z* (%) 1W YTD GBP 1W YTD Z* (%) 1W YTD Non-Fin. All A-AAA BBB Financials All A-AAA BBB Sen Sub Corporate All IG HY Covered All Sub-Sovereigns All CDS IG HY A credit default swap (CDS) is a contract between two parties, relating to a certain issuer, under which one party pays a regular premium (commonly the CDS rate) for an agreed period. If the underlying issuer suffers a credit event during this time, the other party is obliged to pay the agreed face value (usually cash-settled against a recovery value). itraxx and CDX CDS indices represent a basket of issuers CDS. As such, they can be seen as an instrument that measures the credit risk of a general market portfolio. These CDS indices can be traded and used to hedge a market portfolio for smaller transaction costs than would be incurred by using individual CDS contracts. CDX and itraxx IG usually contain 125 issuers, though this number can decrease should a credit event occur. We show CDS index data both in the spread & performance table as well as in the time series charts right underneath. In the Asset swap spread by sectors (investment grade) chart, we show individual sector asset swap spread levels for both EUR and USD-denominated bonds based on average iboxx index levels. The right chart shows one-week changes (in the bars) and yearto-date changes as bullets. The par asset swap spread (ASW) gives an indication of the difference between the yield of the corporate bond and AA rated Libor/swap rates. It is usually expressed in basis points. In an asset swap, a bond is bought and a swap contract entered, in order to convert the paper into a floating rate instrument. The par asset swap spread is the spread applied to the floating leg of the swap so that the net present value of all cash flows is zero. The resulting spread must be used with caution when comparing bonds whose price is significantly away from par. To better compare with other asset classes, we also display total return (i.e. performance) for 1 week and year-to-date. Cash & CDS spreads investment grade (bp) Asset swap spread by sectors (investment grade) (bp) Banks Fin. Services Insurance Basic Mat. Cons. Goods Cons. Svc. Healthcare Industrials Oil and Gas Technology Telecom. Utilities EUR cash EUR CDS USD cash USD CDS EUR USD W YTD Why is the asset swap spread used so broadly and what are the alternatives? The first part of this question has many answers. The most relevant might be that investors can enter real asset swap trades for many bonds. With this trade, they basically turn a fixed-coupon paying bond into a floating rate note which pays them a floating rate (e.g. Euribor) plus the asset swap spread (which may be negative). The asset swap spread thus shows how much more over a floating rate must be paid by a certain issuer to compensate for the additional risk. However, the asset swap transaction does NOT eliminate risks like the default risk, but rather introduces counterparty risk for the swap transaction itself. Other commonly used credit risk measures include the Z-spread, the C-spread, the yield/yield spread and the simple benchmark spread. For those interested, details of these measures and their strengths and weaknesses can be found in a relevant text, for example, The Credit Default Swap Basis by Moorad Choudhry (Bloomberg Press). In recent years, CDS have entered the markets. CDS can be traded for many issuers with various maturities. Investors can use CDS to hedge the default risk of the bonds in their portfolios. CDS are triggered in the case of certain credit events and pay the face value less the recovery value. Additionally, investors can trade CDS indices to hedge a portfolio risk rather than individual issuer risk, if they have a portfolio similar to the index. 11 May

26 How to read the Commodity Monitor The Commodity Monitor gives an overview of key data for commodity markets and, in particular, the four major areas: energy, precious metals, base metals and agriculturals. The price performance information, inventory data and forward curves provide useful information on current market conditions and expected developments. For commodities, spot price performance in most cases cannot be realised by investors. Physically buying and storing oil, for example, is complicated and involves high costs. Therefore both spot price performance and the performance of future-based strategies are tracked for several commodity indices over various periods. We use the S&P GSCI indices as a proxy for the performance of the particular commodity markets in order to ensure a comparable set of indicators. The 12M percentile shows the percentage of closing spot prices that were lower than the current price over the last 12 months. Commodity Monitor While commodities as a whole slid 1.% last week, precious metals surprised with an increase of 7%. ETF Holdings of Gold increased for the 6 th week in a row reaching the highest level since June 215. The energy sector fell by 1.%. In particular crude oil prices declined although US crude oil production slid for the rd consecutive week by.%, dropping to a 7-week low of 9.2 mbpd. Moreover crude oil inventories reported by the DOE dropped slightly for the first time in five weeks, yet most likely due to less imports only. The level of inventories remains at the very high level of more than 5m barrels. Stocks at Cushing increased for the 2 nd consecutive week by while US rig count dropped further by 28 to now 49 only. Oil forward curves steepened further. Coal fell to $4.55 a ton, the lowest level in almost ten years. Copper inventories in China jumped 1.8% in the last week. In the last twelve months 7% were added. Zinc inventories more than doubled in this period. Commodity data Performance spot return vs total return (USD, %) and forward rel. to spot price (%) US WTI crude oil inventories (in mbbl) Inventories last 12M as at 4//216 price percentile spot TR spot TR spot TR spot TR last avg last avg last avg WTI crude Oil $/bbl Brent crude Oil $/bbl Natural Gas $/MMBtu EUA (CO²) /MT n/a -12. n/a -1. n/a -4.5 n/a Coal $/MT n/a 4.2 n/a n/a -2. n/a n/a n/a Electricity /MWh n/a -6. n/a n/a n/a n/a n/a Copper $/MT Aluminium $/MT Iron Ore $/MT n/a 17.5 n/a n/a 1.4 n/a Gold $/tr.oz Silver $/tr.oz Platinum $/tr.oz Palladium $/tr.oz Wheat /bu Corn /bu Soy Beans /bu Sugar /lb year average +/- 1 stdev Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Source: Bloomberg, GSCI, Commerzbank Research as at latest 5Y avg chg 1W%** chg M% chg 1Y% DOE crude oil mbbl 26 Feb DOE Cushing oil stocks mbbl 26 Feb DOE gasoline inventories mbbl 26 Feb DOE natural gas bln cu ft 26 Feb 2,56 1, DOE supply for crude oil days 26 Feb LME aluminum MT 4 Mar 2,798,25 4,814, SFE aluminum MT Mar 29,765 59, LME copper MT 4 Mar 186,7 6, SFE copper MT Mar 5,16 28, LME nickel MT 4 Mar 41, , LME tin MT 4 Mar,75 12, LME zinc MT 4 Mar 465,5 82, SFE zinc MT Mar 261,12 291, Gold holdings tr. oz. 4 Mar 54,96 67, Silver holdings tr. oz. 4 Mar 599,8 61, Platinum holdings tr. oz. 4 Mar 2,197 2, Palladium holdings tr. oz. 4 Mar 2,14 1, USDA wheat stocks/use(%) 9 Feb USDA corn stocks/use(%) 9 Feb USDA soybeans stocks/use(%) 9 Feb USDA cotton stocks/use(%) 9 Feb W 1M 12M YTD This table summarises inventory data for a number of commodities. For agriculturals, a stock-to-use ratio is shown. Crude oil and natural gas figures from the Department of Energy (DOE) are those published for the week ending on the previous Wednesday. The table also shows the number of days of supply for crude oil in the US, relating supply to demand. Forward curves WTI oil 25 Mar-16 Mar-17 Mar-18 4,8 4,7 4,6 4,5 Copper 4,4 Mar-16 Mar-18 Mar-2 Current 1W ago 1M ago fwd spread M* fwd spread 6M fwd spread 12M Apr-16 Apr-17 Apr-18 S&P GSCI weekly index movers (total return, USD) * difference of future to spot price, avg since Jan 2 where available; ** 1M for stocks/use;***est Brent oil Wheat 44 Mar-16 Nov-16 Jul-17 Mar-18 Current 1W ago 1M ago as at 4//216 Wgt*** 1W YTD Wgt 1W YTD S&P GSCI TR Agriculture Energy Corn Heating Oil Cocoa Unleaded Gas Sugar Natural Gas Soybeans Brent Crude Wheat GasOil Coffee Crude Oil Kansas Wheat Precious Metals Cotton Gold Base Metals Silver Lead Livestock Copper Live Cattle Aluminum Lean Hogs Nickel Feeder Cattle Zinc As the weighting of the GSCI sub indices differs significantly, the S&P GSCI weekly index movers list summarises the individual performance contribution, in basis points (bp), of the five subindices, and of the individual commodities, over the last week. The shape of the future curve determines the gain or loss which occurs due to the roll-effect of future-based strategies. This roll effect explains performance differences for spot and total return (TR), i.e. future-based, indices. As futures markets for platinum and palladium are not fully developed, only the M spread is shown. Rising US crude oil inventories typically weigh on WTI spot prices. As inventories for crude oil are subject to seasonality, inventory levels for the past year and a one-standard deviation band around the 5-year average help to put recent trends into perspective. The forward spread describes the steepness of the forward curve relative to the spot price. When a future market shows an upward sloping forward curve as in a normal yield curve it is said to be in contango. The opposite market condition is known as backwardation. This spread can be compared with an appropriate average spread calculated for historical data. For current spreads and historical averages, see the top-right part of the main table. When a market is in contango one may be able to make profit by storing goods. Shorting may be profitable when markets are in backwardation. The crude oil market has been in contango in recent years (see chart). 6% 4% 2% % -2% 6M Spread -4% May 216

27 How to read the Commodity Monitor (cont d) DOE crude oil inventories (mbbl) Inventory data have an important relationship with commodity prices. For some commodities, the size of inventories has increased dramatically over recent years, e.g. crude oil in particular. Apart from energy, we monitor changes in inventories to a 5- year average and also over 1 week, months and 12 months. For precious metals, ETF inflows are shown, as inventories do not have a meaning. In the case of grains, the United States Department of Agriculture (USDA) publishes a stock-to-use ratio which gives a valuable insight into the supply situation In most commodity indices, crude oil has the largest weight. The GSCI index, for example, is dominated by crude oil at a weight of more than 5%. Energy in general accounts for 7% of the index, agriculturals and livestock for 2%, and precious metals and base metals for the remaining c. 1%. When investing in commodities it must be taken into consideration that, apart from the roll effect in crude oil futures, there have also been long periods when, after adjusting for inflation, investors did not profit from storing crude oil Measurement units depend on the commodity. Stocks of oil are measured in millions of barrels, and gas in billions of cubic feet. For crude oil, days of supply (which takes cyclical storage into account) is also stated. The units for metals vary, precious metals are quoted in troy ounces, and base metals in metric tons % 6% 4% 2% % 6 month rolling equity market correlation CPI adjusted oil price oil price (US$, rhs) -2% -4% -6% Crude oil Gold Commodities have traditionally been a useful diversification asset when building optimal portfolios, due to the low or even negative correlation of commodities with the equity market (here, the MSCI World Net USD). However, in recent years, correlations between crude oil and equities have increased to almost 8%. Gold has a much lower correlation with equities. 11 May

28 How to read the Real Estate Monitor The Real Estate Monitor gives an overview of the main developments in real estate markets across the world. The focus of the monitor is on UK and US indicators for prices and activity, in commercial and non-commercial real estate markets. The Investment Property Database (IPD) publishes commercial real estate indices on a regular basis. Here, we show five regional All Property Total Return indices, which measure the total annual return to real estate assets held directly in professionally managed portfolios. IPD data do not include any bonds, cash, derivatives, or REIT shares. Real Estate Monitor DM REITs declined strongly last week. They underperformed their equity market peers by 1.7pp as 1-year US-Treasury yields rebounded substantially from sub 1.55% levels to 1.75%. However, y-t-d they are still 1.5pp ahead of equities. Last week s decline of REITs alongside global equities is also illustrated by all regions trading below the 5 th 12-month price percentile. From a regional point of view EM REITs fared relatively better than both EM equities and DM REITs. Amongst DM REITs, it was Japan that took the strongest beating, dropping by 1.%. In contrast, Europe ex UK and Hong Kong held up relatively well and outperformed their equity market benchmarks considerably. The UK RICS housing market survey stopped its journey north in the past few months and in January stagnated at a solid 49% for the third consecutive month. Moreover, the UK Rightmove house price index increased by 7.% in February, more than January s 6.5%, resuming its general upward trend. Price and activity indices EPRA/NAREIT indices vs regional equity indices 12M Ann Total return 1 (%) Performance vs equity (%) 6 Valuation / fundamental data 1W 1M 1W 1M YTD yld 2 Exp. Sov yld 4 as at 2/2/216 Percentile Vol (%) YTD P/E ratio P/B ratio Dvd RoA 2 RoE 2 Debt/TotA 5 DM REITs Europe ex UK Germany France UK US Asia Pacific ex Japan Singapore Hong Kong Japan EM REITs in local currency 2 in % Expected dividend yield as per the upcoming reporting period 4 generic ten-year sovereign yield in %. For Europe ex UK, the German ten-year yield is shown 5 Debt/total assets ratio in % 6 vs (in order) MSCI World, Stoxx ex UK, DAX, CAC4, MSCI UK, S&P 5, MSCI Asia Pacific ex Japan, Straits Times, Hang Seng, Topix, MSCI EM Commercial RE: IPD all property total return (%)* 2 1 US mortgage applications** / building permits The Halifax House Price Index reflects developments in standardised house prices in the UK. The basis for the monthly report is Halifax s mortgage approval data, hence the index is based on Halifax s market share of c. 25% in the UK mortgage market. The index measures the typical house price, where a specific value is attributed to each of twelve characteristics through a multivariate regression analysis. The coverage of approved rather than completed mortgages implies on the one hand that some cases are covered which will not be completed, but on the other, it allows for more up-to-date data Australia (12M) Canada (12M) New Zeald (12M) Ireland (QoQ) UK (QoQ) US (QoQ) US S&P/Case-Shiller & Moody's commercial price index S&P/Case-Shiller House Price Index UK Halifax house price index Source: Bloomberg, Commerzbank Research Moody's commercial price index Halifax house price index SA YoY chg (rhs, %) Applications for purchases index SA Building permits (in k) Total index / purchases index (rhs) US new and existing home sales New home sales in thsd SAAR Existing home sales in mn SAAR (rhs) UK RICS sentiment and BBA loans indicator RICS housing market survey SA *Investment Property Database, **Mortgage Bankers Association Housing loans approved in bn GBP (rhs) The EPRA/NAREIT Indices reflect trends in real estate equities. They are shown for the five major developed economic regions and for an aggregate. The direct comparison with corresponding overall equity indices allows for a judgment of real estate performance. A Real Estate Investment Trust (REIT) is a listed real estate company that owns investment-grade commercial or residential real estate. Therefore, a REIT s profit is based on income from leases and the sale of property, and represents the performance of real estate. REITs must satisfy certain requirements, such as distributing at least 9% of their taxable income to their shareholders. Due to the high correlation with equity markets, the real estate market indication provided by REITs contains some noise The UK RICS Sentiment Indicator shows the net balance from a survey asking in which direction UK house prices moved in the last three months. It is published monthly by the Royal Institution of Chartered Surveyors (RICS) and is computed through a net balance approach, the balance being the proportion of surveyors reporting a rise less those reporting a fall. A rise in housing prices indicates a strong housing market, which in turn generally reflects a strong overall economy, though supply factors also play a part. The British Bankers Association (BBA) indicator represents the value of loans approved for house purchases in GBPbn. The BBA loans indicator can be viewed as a leading indicator for the housing sector as it indicates trends in real estate activity. Furthermore, the demand for mortgages supports the pound and leads, in healthy economic conditions, to rising bond yields. An increase in the BBA indicator typically leads an upward trend in the UK stock market, especially in the financing and construction sectors May 216

29 How to read the Real Estate Monitor (cont d) Real Estate indices are generally prone to seasonality. House prices typically tend to be higher in the spring and summer months than in autumn and winter due to seasonal demand patterns, the impact of weather on construction and other influences. For this reason, most indices reported in the Real Estate Monitor are seasonally adjusted (SA). Commercial RE: IPD all property total return (%)* The development of residential housing markets in ten metropolitan 2 regions across the United States is 1tracked by the S&P/Case-Shiller Composite 1 Home Price Index. The index measures single family home prices on a -1 monthly basis. It is based on the repeated sales methodology, i.e. on measures of the price change between two transfers of the same object Australia (12M) Canada (12M) New Zeald (12M) Ireland (QoQ) UK (QoQ) US (QoQ) US S&P/Case-Shiller & Moody's commercial price index S&P/Case-Shiller House Price Index Moody's commercial price index US mortgage applications** / building permits Applications for purchases index SA Building permits (in k) Total index / purchases index (rhs) US new and New hom The Moody s/real commercial property price index tracks commercial investment properties in the United States. It is based on actual transaction of commercial real estate compiled by Real Capital Analytics and is calculated using the repeat sales method to provide an accurate indication of price changes. The S&P/Case-Shiller index for residential property prices is released with a -month lag, while the Moody s/real index for commercial property prices is made available with a 2-month lag. For the latter index, for example, data for April would be released in June. In the chart US new and existing home sales, we present figures on existing home sales and new housing units sold. The new housing units sold only include one family houses built for sale. On a monthly basis, the US Census Bureau collects information on physical characteristics and prices of new onefamily houses through interviews with the US S&P/Case-Shiller & Moody's commercial price index 24 builders or owners of a national sample of new 22 houses. The 2 sale of a new house includes houses which 18are under construction or have 16 not yet been started. The National Association 14 of Realtors collects monthly data on existing 12 home sales, which include mainly closed 1 transactions. 8 Therefore, developments at the level of new home sales usually lead S&P/Case-Shiller House Price Index developments in existing home sales by a month or two. Moody's commercial price index The US Mortgage Bankers Association publishes multiple mortgage indices on a weekly basis. We report here the Mortgage Purchase Index and the Mortgage Applications Index. The former index includes all mortgage applications, whether for a purchase or to refinance, while the latter includes only mortgage applications for a purchase. The purchase index serves as an early indicator of real estate trends. It reflects the economic situation and also market sentiment, and is an early indicator of house purchases. On the other hand, the ratio of total mortgage applications to applications for purchases reflects the degree of refinancing activity. The number of residential building permits issued for new private housing units is an indicator of construction activity, which typically leads most other types of economic production. The data is published monthly as a component series of the US Conference Board Leading Index and is based on census surveys. US new and existing home sales New home sales in thsd SAAR Existing home sales in mn SAAR (rhs) 11 May

30 How to read the Emerging Markets Monitor Emerging Markets Monitor The Emerging Markets Monitor tracks both macroeconomic and financial market developments. This is to take account of the growing importance of EM for global market developments and the global economy. It provides readers the opportunity to quickly grasp economic activity and inflation surprises and crisis warning signals. The Monitor concludes with price actions across EM asset classes. The first page depicts macro-fundamental developments, spanning economic and inflation surprises to our proprietary EM early warning index. EM macro data once again diverged last week. On the one hand, CEE GDP figures were quite upbeat with e.g. Hungarian Q4 GDP printing a.2% y-o-y growth rate (vs 2.5% expected) and Poland also surprising to the upside at.9% y-o-y (vs.8% expected). On the other hand, Asian and LatAm data releases were quite scarce as e.g. Indian and Mexican industrial production both surprised to the downside. Last week s risk-off mood did of course not spare EM assets. Equities and REITs were by far the worst asset class within the EM universe as all EM assets declined in value last week. EM fx held up relatively well given that Fed rate hike expectations were even replaced by Fed rate cut expectations over the course of last week. Last week s weaker Indian industrial production figures also resulted in Indian assets performing poorly. As such, Indian equities were underperforming the most last week and the rupee was also amongst the worst performers in EM fx. Emerging Markets macroeconomic indicators Surprise indicator for EM activity (cumulative) Surprise indicator for EM inflation (cumulative) Both economic and inflation surprise indicators are constructed using our established methodology for US and EMU surprises (see p. 15). For reference, the surprise factors are calculated as Actuali Forecast i Surprise F actori Weighti StDev(Actuali Forecast i ) The weighting methodology follows a stringent approach. We weight each indicator by importance and the timeliness of the datapoint. Finally, larger economies indicators receive a higher weighting. Building on the surprise factor described above, we calculate surprise indicators for economic activity and inflation for the three EM regions: EMEA, LatAm and EM Asia. The number of activity and inflation indicators and the country weight factor are illustrated in the table below EMEA LatAm EM Asia Top 15 activity surprises of the last four weeks Date EM activity Period Wgt %* Cons. Actual Surprise** 4//216 Mexico FA Investment %YY Dec //216 Hungary Trade Bal. m EUR Dec //216 Poland Man. PMI Feb /2/216 Colombia Unemp Rate % Jan /2/216 Brazil Unemployment % Jan /2/216 South Africa Unemp. % Dec /2/216 Philippines Trade Bal. m USD Dec , /2/216 Mexico Retail Sales %YY Dec /2/216 Turkey Consumer Conf. Feb /2/216 Colombia Consumer Conf. Jan /2/216 Poland Employment Chg %YY Jan /2/216 India Trade Bal. (m USD) Jan 6.4-9, , /2/216 Peru Activity %YY Dec /2/216 Czech Ind. Prod. %YY Dec /2/216 Czech Unemployment % Jan EM early warning index Global EM currency risk index Average since EMEA LatAm EM Asia Top 15 inflation surprises of the last four weeks Date EM inflation Period Wgt %* Cons. Actual Surprise** 5//216 Colombia CPI Feb %YY 4//216 Russia CPI %YY Feb //216 Turkey CPI %YY Feb //216 South Korea CPI %YY Feb /2/216 South Africa PPI %YY Jan /2/216 Czech PPI %YY Jan /2/216 Malaysia CPI %YY Jan /2/216 South Africa CPI %YY Jan /2/216 Poland PPI %YY Jan /2/216 Russia PPI %YY Jan /2/216 India PPI %YY Jan /2/216 Poland CPI %YY Jan /2/216 Hungary CPI %YY Jan /2/216 Mexico CPI %YY Jan /2/216 Colombia core CPI %MM Jan EM early warning index (individual country matrix) 1M Change M Change MTD Current account Money supply Inflation Industrial production Exports Short-term debt Domestic credit Non-perf loans Economic surprises REER FX implied vol Equity market Global risk BRL ZAR CLP COP TRY PLN RUB IDR CZK RON INR MXN KRW ILS HUF SGD MYR THB PHP Source: Bloomberg, Commerzbank Research; *weights correspond to indicator in respective region; **surprise = ± (actual consensus) / standard deviation (actual consensus); The tables show the data releases with the largest impact on the respective surprise indicator over the past four weeks. For data releases that make it into this table, at least one of the following two criteria will tend to be true: 1) the actual reading deviates considerably from the consensus forecast; and 2) the indicator has been assigned a large weight. We show top surprises for both EM activity and inflation indicators across all three regions. Apart from depicting what period the indicator covers, we show surprises (in standard deviations) and the weights of each datapoint. These correspond to the weight of the indicator in each region. The top three surprises are highlighted in bold. We also show simple cumulative surprises. For this, we use an arbitarily selected starting point, so the level of the indicator itself is not meaningful. What matters is the change in the indicator. The change in the indicator is given by the sum of the surprise factors of the indicators of the respective EM region published on that day. If no data is released then the surprise factor for that day is zero. A rise in the EM activity and inflation indicator means that macro and inflation data have generally surprised to the upside, or vice versa. 11 May 216

31 Emerging Markets Monitor Cross Asset Strategy Cross Asset Monitor Guide EM macro data once again diverged last week. On the one hand, CEE GDP figures were quite upbeat with e.g. Hungarian Q4 GDP printing a.2% y-o-y growth rate (vs 2.5% expected) and Poland also surprising to the upside at.9% y-o-y (vs.8% How expected). to read On the other hand, Emerging Asian and LatAm Markets data releases Monitor were quite scarce as e.g. Indian and Mexican industrial production both surprised to the downside. Last week s risk-off mood did of course not spare EM assets. Equities and REITs were by far the worst asset class within The lower part of the first page of the Emerging Markets the EM universe as all EM assets declined in value last week. EM fx held up relatively well given that Fed rate hike Monitor illustrates our proprietary EM early warning We select macroeconomic data series that have a expectations were even replaced by Fed rate cut expectations over the course of last week. index. Firstly, it shows an individual country heat map logical link to financial markets in EM, for example to Last (see week s below). weaker Secondly Indian we industrial show the production aggregate EM figures index also resulted EM fx. in Indian assets performing poorly. As such, Indian equities as a were historical underperforming time series since the most last This week is a and simple the rupee was also amongst the worst performers in EM fx. We also incorporate financial market indicators that Emerging average Markets of all macroeconomic the EM countries indicators percentile ranks. tend to be available on a daily basis and do not suffer Surprise indicator for EM activity (cumulative) Surprise from indicator the longer for publication EM inflation lags (cumulative) typically encountered for economic data Jan-1 5 Jul-1 Jan-14 Jul-14 Jan-15 Jul-15 Jan-16 Top 15 activity surprises of the last four weeks Global EM currency risk index Average since 1996 Date EM activity Period Wgt %* Cons. Actual Surprise** 4//216 Mexico FA Investment %YY Dec //216 Hungary Trade Bal. m EUR Dec //216 Poland Man. PMI Feb /2/216 Colombia Unemp Rate % Jan /2/216 Brazil Unemployment % Jan /2/216 South Africa Unemp. % Dec /2/216 Philippines Trade Bal. m USD Dec , /2/216 Mexico Retail Sales %YY Dec /2/216 Turkey Consumer Conf. Feb /2/216 Colombia Consumer Conf. Jan /2/216 Poland Employment Chg %YY Jan /2/216 India Trade Bal. (m USD) Jan 6.4-9, , The heatmap shows percentile rankings across countries and the respective indicators. A higher reading suggests that the figure reached higher stress-like levels. The first column shows the EM early warning index overall EM early warning index for each country. 7 As mentioned above, this is a simple average 65 across all macroeconomic and market factors ranks of the country in question. 15/2/216 Peru Activity %YY Dec /2/216 Czech Ind. Prod. %YY Dec /2/216 Czech Unemployment % Jan EM early warning index EMEA LatAm EM Asia The main heat map of the EM early warning index shows our candidate countries ranked by risk for EM asset classes such as, e.g., depreciation of EM fx over the next few months. The contributions of the various factors can also be seen, with darker colours signifying greater risk. 1M and M changes correspond to the changes over the last calendar month and the last three months, while MTD changes correspond to changes in the indicator since the start of the month. A positive change indicates increasing stress. In contrast, a negative change in the index suggests abating stress for the respective EM country. Global EM currency risk index Average since 1996 EM early warning index (individual country matrix) BRL ZAR CLP -6 over -1 2 the -1 5 entire 5 48 dataset For 15 example, we 57 rank a COP TRY country s risk factors against their own history first, and PLN RUB IDR 57 then 1 4 re-do this ranking process for 64 8 all 56countries CZK RON-1 56 covered in 45the EM 6 88early warning 9 5 index using the INR MXN 52 available history KRW ILS EMEA LatAm EM Asia HUF SGD MYR THB PHP Date EM inflation Period Wgt %* Cons. Actual Surprise** Percentile rankings provide a measure of risk. For Top 15 factors inflation where surprises a lower value of the is last more four likely weeks to give cause for concern, the risk level is given as 1 minus the percentile ranking. For example, for factors such as fx implied volatility, where higher values suggest an elevated level of risk, the risk measure for the variable is simply given by the percentile ranking. For factors such as industrial production growth, a lower value is more likely to give more concern, i.e. we calculate the risk factor as 1 minus the percentile ranking. EM early warning index (individual country matrix) Source: Bloomberg, Commerzbank Research; *weights correspond to indicator in respective region; **surprise = ± (actual consensus) / standard deviation (actual consensus) ; The first step in constructing the early warning indices involves the ranking of each data point with respect to its own history. The second step is a second ranking process in which we arrange the time-series percentile ranks cross-sectionally across all countries. Hence, the second ranking procedure provides the percentile 1M Change M Change ranks of all country percentile ranks for a given variable 5//216 Colombia CPI %YY Feb //216 Russia CPI %YY Feb //216 Turkey CPI %YY Feb //216 South Korea CPI %YY Feb /2/216 South Africa PPI %YY Jan /2/216 Czech PPI %YY Jan /2/216 Malaysia CPI %YY Jan /2/216 South Africa CPI %YY Jan /2/216 Poland PPI %YY Jan Finally, risk rankings for all macroeconomic and market factors for an individual country are simply averaged to generate an overall risk rating on a scale from to 1 for the country in question, denoted by their fx code. 17/2/216 Russia PPI %YY Jan /2/216 India PPI %YY Jan /2/216 Poland CPI %YY Jan /2/216 Hungary CPI %YY Jan /2/216 Mexico CPI %YY Jan /2/216 Colombia core CPI %MM Jan M Change MTD M Change Current account MTD Money supply Current account Inflation Industrial production Money supply Exports Inflation Short-term debt Industrial production Domestic credit BRL ZAR CLP COP TRY PLN RUB IDR CZK RON INR MXN KRW ILS HUF SGD MYR THB PHP Exports Non-perf loans Economic surprises Short-term debt REER Domestic credit FX implied vol Non-perf loans Equity market Economic surprises Global risk REER FX implied vol Equity market Global risk 11 May 216 1

32 How to read the Emerging Markets Monitor (cont d) The second page of the Emerging Markets Monitor illustrates the recent performance of EM asset classes. Starting with performance across EM asset classes, this monitor includes the development of EM fx, equities, CDS and external and local sovereign debt as well as corporate credit. We show both 1-week and 4-week changes as a percentage, as well as the y-t-d change in these asset classes. The level of assets is included where it is meaningful. Cross Asset EM performance is shown both on an absolute and a relative basis in USD terms. We show 1-week, 4-week and y-t-d changes for various EM asset classes. The right hand chart depicts the development of various EM asset classes vs. their DM equivalents over a rolling 12-month horizon. Cross Asset Emerging Markets Asset Class Overview Emerging Markets Cross Asset performance (TR, USD %) Relative EM Cross Asset performance (TR, USD)* Relative EM Performance vs DM (USD), /1/215 = 1 FX Credit (hcy) Sovereign Sovereign REITs Equities 75 debt (hcy) debt (lcy) Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 1W 4W YTD (rhs) Equity Local debt External debt EM fx Emerging Markets fx (spot, vs USD, %) Relative EM equity performance (MSCI TR indices, USD) Relative EM Performance vs DM (MSCI World, USD), 1/1/215 = PL ID TW ZA MY KR CN TR IN RU BR MX Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 1W 4W YTD (rhs) EMEA LatAm EM Asia Emerging Markets equity (MSCI TR indices, local, %) Emerging Markets equity (MSCI TR indices, USD, %) TW MY PL BR MX ID ZA KR RU TR CN IN TW PL MY ID ZA BR TR KR CN RU MX IN 1W 4W YTD (rhs) 1W 4W YTD (rhs) Emerging Markets CDS (5-year, bp) Emerging Markets external debt spreads (1-year, bp)** The development of EM fx (vs. USD) is also shown by 1-week, 4-week and y-t-d changes. Note that the universe of EM countries remains the same throughout the Emerging Markets Asset Class Overview, where data is available. Just like in the EM Cross Asset chart above, we show the relative performance of EM equity vs. its DM equivalent over a rolling 12-month horizon. In order to gauge regional differences, this graph provides an overview of the performance of the three EM regions vs. DM equities. In addition to the relative performance, we also demonstrate the 1-week, 4-week and y- t-d absolute performance of EM equities. Note that this is shown for the most important MSCI EM countries in both local currency and USD terms. -1 BR ZA RU TR ID MX MY CN PL KR -4-1 BR ZA TR ID RU MX PL -5 Level YTD 1W (rhs) 4W (rhs) Emerging Markets local rates (1Y, %) BR TR RU ZA ID IN MX MY PL CN KR TW Level YTD 1W (rhs) 4W (rhs) 6 Emerging Markets local rates curves (2s1s, bp) MX PL ZA BR ID TW IN CN KR RU TR Note that the setup of the charts for EM CDS, external debt spreads, EM local rates and EM local rates curves is slightly different. Apart from 1-week, 4-week and y-td changes, we also show the levels of spreads, yields and yield curves. Level YTD 1W (bp, rhs) 4W (bp, rhs) Level YTD 1W (rhs) 4W (rhs) Source: Bloomberg, Commerzbank Research; * Equities: vs MSCI World, FI: vs EFFAS Global ** 1-year yield on USD sovereign bonds vs 1-year US-Treasury yield For the local sovereign debt space we again illustrate the level of 1- year sovereign bonds, denominated in local currency. The usual 1- week, 4-week and y-t-d changes are also shown. The last graph, depicting the development and the level of the 2s1s curves in the local sovereign debt space, is important information for investors looking to buy into EM debt. Generally speaking, the higher the level of the curve, the better. This is because investors will benefit from rolling down the yield curve. For EM fixed income we depict several measures of performance. Firstly, we show the level of 5-year CDS prices and 1-year external debt spreads (of EM sovereign bonds denominated in USD) over 1-year US-Treasuries. We also show 1-week, 4- week and y-t-d changes of both external debt and CDS spreads May 216

33 Reference to first page of disclaimer Distribution of ratings: Number of recommendations from Commerzbank, CM- Research, at the end of the first quarter (45.%) Buy / Add 22 (26.8%) 78 (42.9%) Hold 14 (17.9%) 22 (12.1%) Sell / Reduce 4 (18.1%) Source: Commerzbank Research thereof recommendations for issuers to which investment banking services were provided during the preceding twelve months This document has been created and published by the Corporates & Markets division of Commerzbank AG, Frankfurt/Main or Commerzbank s branch offices mentioned in the document. Commerzbank Corporates & Markets is the investment banking division of Commerzbank, integrating research, debt, equities, interest rates and foreign exchange. 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34 Additional notes to readers in the following countries: Germany: Commerzbank AG is registered in the Commercial Register at Amtsgericht Frankfurt under the number HRB 2. Commerzbank AG is supervised by both the German regulator, Bundesanstalt für Finanzdienstleistungsaufsicht (BaFin), Graurheindorfer Strasse 18, 5117 Bonn, Marie-Curie-Strasse 24-28, 649 Frankfurt am Main and the European Central Bank, Sonnemannstrasse 2, 614 Frankfurt am Main, Germany. United Kingdom: This document has been issued or approved for issue in the United Kingdom by Commerzbank AG London Branch. Commerzbank AG, London Branch is authorised by Bundesanstalt für Finanzdienstleistungsaufsicht (BaFin), and the European Central Bank and is subject to limited regulation by the Financial Conduct Authority and Prudential Regulation Authority. Details on the extent of our regulation by the Financial Conduct Authority and Prudential Regulation Authority are available from us on request. 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Please contact the Corporates & Markets division of Commerzbank AG or Commerzbank AG, Tokyo Branch for inquiries on the availability of such instruments. [Commerzbank AG, Tokyo Branch] Registered Financial Institution: Director of Kanto Local Finance Bureau (Tokin) No. 641 / Member Association: Japanese Bankers Association. Australia: Commerzbank AG does not hold an Australian financial services licence. This document is being distributed in Australia to wholesale customers pursuant to an Australian financial services licence exemption for Commerzbank AG under Class Order 4/11. Commerzbank AG is regulated by Bundesanstalt für Finanzdienstleistungsaufsicht (BaFin) under the laws of Germany which differ from Australian laws. Commerzbank AG 216. All rights reserved. Version 9.25 Commerzbank Corporates & Markets Frankfurt Commerzbank AG London Commerzbank AG New York Commerz Markets LLC Singapore Commerzbank AG Hong Kong Commerzbank AG DLZ - Gebäude 2, Händlerhaus Mainzer Landstraße Frankfurt PO BOX Gresham Street London, EC2P 2XY 225 Liberty Street, 2nd floor, New York, NY , Robinson Road, #12-1 Singapore th Floor, Lee Garden One Hysan Avenue, Causeway Bay Hong Kong Tel: Tel: Tel: Tel: Tel: May 216

35 Cross Asset Strategy Publications & Team Core Research Publications Cross Asset Monitor A weekly report which provides an overview of the development of different asset classes and helps in judging market sentiment. It monitors performance, volatility and correlation across asset classes, tracks a wide range of sentiment indicators, global fund flows, speculative investor positioning and the major drivers of individual asset classes. Once a month it is supplemented by the performance chartbook, which provides an overview of the absolute and relative performance development across and within asset classes. It helps identifying short- and long-term trends. The Cross Asset Monitor guide a compilation of how to sections focusing on specific pages of the Monitor may prove useful in making the publication accessible, even to those investors who may not initially be familiar with all areas covered. Cross Asset Outlook This monthly publication aims to provide a consistent investable view on markets across asset classes. It combines the macro outlook from our economists with the recommendations of the individual asset class strategists and the relative attractiveness of asset classes signalled by our models. Cross Asset Feature Ad hoc special reports on cross asset themes. For a complete list of available reports please see our website at Joint Venture Research Publications Cross Asset Fixed Income Joint Venture iboxx index roll preview Cross Asset Strategy publishes three regular fixed income joint venture publications: iboxx index roll preview For a detailed analysis of roll impacts on individual indices, see the index roll report on our 'GlobeWeb' website at The Team Dr. Bernd Meyer, CFA Head of Cross Asset Strategy dr.bernd.meyer@commerzbank.com Heike Hauer Senior Cross Asset Strategist heike.hauer@commerzbank.com Alexander Krämer, CFA Senior Cross Asset Strategist alexander.kraemer@commerzbank.com Ulrich Urbahn, CFA Senior Cross Asset Strategist ulrich.urbahn@commerzbank.com Stephan Appelhans, CEFA Senior Cross Asset Strategist stephan.appelhans@commerzbank.com Maximilian Kettner Cross Asset Strategist maximilian.kettner@commerzbank.com Andy Lee Senior Cross Asset Strategist andy.lee@commerzbank.com Peter Werno, CFA Senior Cross Asset Strategist peter.werno@commerzbank.com 11 May 216 5