Global Risk Monitor. Liquid Markets Analytics

Transcription

1 Liquid Markets Analytics Global Risk Monitor This sales note is produced by Nomura Equities and is not a product of the Nomura Research Department. Please read the important disclaimers on page 8. Volatility in global indices increased over the month of February, but the timing and severity of the increase varied by region. In Europe, concerns around Greek credit caused volatility to spike sharply early in the month; Japanese stocks mirrored the volatility of the yen mid-month; volatility in Asia Ex-Japan climbed steadily higher throughout the month; and American indices retreated at the end of the month from a big jump in volatility early on. Nomura risk models anticipate steady to moderately increasing risk in the medium-term. This month saw a significant further shift from common (market) to specific (idiosyncratic) risk. Macro factors like credit still play a big role in large cap risk. Risk is still driven by Financials, despite relief in the sector s volatility over the past year. Read on for more details. Figure 1: Percent Change in Index Volatility Over February Katy Hinton kathryn.hinton@nomura.com Liquid Markets Analytics TradeSpexSupport@nomura.com New York London Hong Kong Tokyo MSCI Asia Ex-Japan Euro Stoxx 50 DJ Stoxx 600 FTSE All Share MSCI Europe S&P/ASX 200 Hang Seng MSCI EM S&P/TSX Composite S&P 500 Nikkei 225 Topix Mumbai Source: Nomura TradeSpex

2 Global Risk Summary: Volatility Increases Continue Figure 1 above shows the one month change in 21-day annualized historical volatility for our universe of regional and global indices. There was an increase in global volatility for the second consecutive month. Only the Topix became less volatile, and its volatility decrease was slight (-1.). Figure 2 shows the current, thirty month mean and thirty month maximum volatilities for each index. Most indices are slightly above six month mean values and slightly below six month max values. Figure 2: February Change in Volatility (Top) and Current (3/1/2010), 30M Max and 30M Mean Volatility for Regional and Global Indices (Bottom) Americas Europe Asia Ex Japan Global Max Mean Current 1 S&P/TSX Composite S&P 500 MSCI Europe DJ Stoxx 600 Euro Stoxx 50 FTSE All Share MSCI Asia Ex- Japan Hang Seng S&P/ASX 200 Nikkei 225 Topix MSCI EM Source: Nomura TradeSpex Figures 3c-f below show rolling 21-day annualized historical volatility averages for the past six months for the set of indices. Diamond markers indicate short- and medium-term risk estimates from Nomura s Statistical Risk Model (SRM) suite for each index as of 3/1/ Figures 3a-b show the same measures for global indices, but employ only medium-term risk estimates from the SRM suite. Unlike in recent months, volatility trends in February varied regionally: for example, Japanese index volatility plummeted at the beginning of the month and then rose sharply mid-month with the yen, while indices in Asia outside of Japan gradually increased in volatility over the month. 1 A detailed description of the SRM suite is available in the white paper Nomura Statistical Risk Model Suite I: A First Look (R. Malhotra et al), issued 2/26/2009. Contact your Nomura representative for a copy. 2

3 Figures 3a-f: Annualized 21D Historical Volatility and SRM Short- and Medium-Term Risk Estimates ( ) for Indices in Global Developed Markets (a), Global Emerging Markets (a), Americas (c), Europe (d), Asia Ex-Japan (e), Japan (f), as of 3/1/2010 Global Emerging Markets MSCI EM /1/ /30/2009 1/29/2010 3/30/2010 5/29/ /1/ /30/2009 1/29/2010 3/30/2010 5/29/2010 Americas S&P /1/ /30/2009 1/29/2010 3/30/2010 5/29/2010 FTSE All Share DJ Stoxx 600 Europe Euro Stoxx /1/ /30/2009 1/29/2010 3/30/2010 5/29/2010 Asia Ex-Japan MSCI Asia Ex-Japan Hang Seng S&P/ASX /1/ /30/2009 1/29/2010 3/30/2010 5/29/2010 Nikkei 225 Japan Topix /1/ /30/2009 1/29/2010 3/30/2010 5/29/2010 Source: Nomura TradeSpex This past month: Global: volatility increased in big steps on January 20 and February 4, and maintained higher levels for the duration of February. Medium-term SRM risk is anticipated at levels 37. higher than current volatility. MSCI Emerging Markets volatility, meanwhile, increased gradually from late January through late February before decreasing slightly at the end of the month. Its medium-term estimate is only slightly (16.) above current volatility levels. Americas: Both the S&P 500 and the S&P/TSX Composite increased in volatility from mid-january to early February before decreasing slightly at the end of February. Unusually, the volatility profiles of these indices resembled that of the MSCI Emerging Markets Index more closely than indices in any other region. SRM short-term volatility estimates are slightly below current volatilities for the first time 3

4 since this publication began in July of 2009, but medium-term estimates anticipate an increase in risk thereafter. Europe: European indices endured a sharp increase in volatility around concerns over ballooning public debt in Greece. The Euro Stoxx and DJ Stoxx 600 indices, which have a small weight of Greek names, had more exaggerated volatility increases than the FTSE All Share index, which has no Greek names. Index volatilities then fluctuated in a narrow band for the rest of the month. Short-term SRM risk estimates are roughly in line with current volatilities, while medium-term estimates anticipate a slight rise in risk. Asia Ex-Japan: Volatility in Asia Ex-Japan indices increased gradually without the sharp spikes and cliffs observed in indices in other regions. Their short-term SRM estimates are roughly in line with current volatilities, but medium-term estimates anticipate a slight rise in volatility after that. Japan: Both Japanese indices, Nikkei and Topix increased sharply in volatility over stock news and yen volatility. Short- and mediumterm SRM estimates are around current volatilities, suggesting stable volatility around 21% for the next couple months. The SRM suite estimates the overall risk profile of an asset or portfolio, including tail risk. It is thus frequently 5-1 higher than the 21-day standard deviation, which ignores tail risk. Figure 4 plots the Chicago Board Options Exchange Volatility Index (VIX), which estimates the implied annualized volatility of the S&P 500 over the subsequent 30-day period, against 21-day (annualized) volatility. The VIX is scaled by a factor of the square root of (21/30) to convert from a 30-day forecast to a 21-day one, so its levels do not match those shown on Bloomberg. Implied volatility rose with S&P realized volatility in early February, then decreased later with a slight lag behind the S&P s track. It finished the month in line with S&P volatility. Figure 4: VIX from 10/1/2009 to 3/1/ Annualized Volatility (%) 1 MXWO Vol 21D Realized SPX Vol First of Month 10/1/ /20/2009 1/9/2010 2/28/2010 Source: Bloomberg, Nomura TradeSpex 4

5 Risk Decomposition Figures 4a-f decompose regional and global index risk as estimated by Nomura s Statistical Risk Model (SRM) suite into sector components. The dark red bars represent the fraction of total portfolio risk in a particular sector (weight of names in that sector multiplied by risk sensitivity of that sector), while the dark gray bars represent the estimated annual risk of each sector. Horizontal bars mark the total portfolio risk level for comparison. Figures 5a-d: SRM Risk Estimates for Regional and Global Indices by Bloomberg Sector and Market Cap Bucket, as of 3/1/2010 ( Figures 5a-b use Nomura s SRM Medium-Term global model, while Figures 5c-d use SRM Short-Term regional models) Risk by Sector Contribution to Total Risk (left axis) Annual Risk (right axis) Risk by Market Cap Financial Energy Industrial Non-cyclical Basic Materials Cyclical Communicati ons Technology Utilities Diversified More than $10B - $5B - $10B $1B - $5B 23% 22% 22% 21% 21% 19% 19% 18% 18% S&P 500 Risk by Sector S&P 500 Risk by Market Cap Financial Non-cyclical Technology Energy Industrial Communicat ions Cyclical Basic Materials Utilities Diversified More than $10B - $5B - $10B $1B - $5B 1 MSCI Europe Risk by Sector MSCI Europe Risk by Market Cap Financial Non-cyclical Basic Materials Energy Industrial Communicat ions Cyclical Utilities Technology Diversified $10B - $5B - $10B $1B - $5B 24% 24% 23% 23% 22% 22% 21% 21% Source: Nomura Liquid Markets Analytics Risk estimates for additional securities, indices, or portfolios of securities can be obtained with the TradeSpex Portfolio application. 5

6 Statistical Risk Factor Analysis: Risk is expected to shift further towards statistical factors Risk attribution to statistical factors can help describe the concentration of market risk: the number of non-noise statistical factors and tends to go down and the average pairwise correlation between names and percentage of risk that is common across securities tends to go up during large market-wide swings in prices. Nomura s SRM suite uses random matrix theory to dynamically select the number of informational risk factors relevant to a given universe of stocks while excluding noise, which allows investors to compare risk factor characteristics across sectors, countries and other aggregations. Tables 1a and 1b show a breakdown of risk for S&P 500 and MSCI World sectors using the SRM Americas Short-term and SRM Global (medium-term) models, respectively. Table 1a and 1b: Risk Factor Analysis by Bloomberg Sector for Names, as of 3/1/2010 Sector Names Weight Factors Total Risk % Common Risk % Specific Risk Avg Corr Basic Materials % % 73.7% 26.3% 3% Communications % % 56.6% 6% Cyclical % % 53.2% 46.8% 3% Non-cyclical % % 48.1% 51.9% 7% Energy % % % Financial % % 35.6% Industrial % % 41.4% Technology % % 49.8% 50.2% 18% Utilities % % 51.1% 12% Sum/Median % % % MSCI EM % 62.3% 37.7% 6% *Omitting Diversified names Source: Nomura Liquid Markets Analytics Expected risk is spread across a large number of factors, but many of these factors are common across multiple sectors. Overall forwardlooking risk estimates are down since last month, with a significant shift from common (market) to specific (idiosyncratic) factors in almost ever sector. Despite this shift, most risk is still anticipated to be a result of common factors, except in the Technology and Utilities sectors. Average correlation between names is low, but highest (23%) in the Energy and Technology sectors because of its dependence on commodity prices. Fundamental Risk Factor Analysis The TradeSpex suite allows users to construct customized risk models with user-specified model universes, time horizons, half-lives and fundamental factor sets. Models can be stored under users profiles for later use within the TradeSpex application for risk analysis, portfolio construction, hedging and optimization. In order to identify the factors with the greatest effect on portfolio variability, individual models were created for each of thirty fundamental factors, using S&P 500, and MSCI Emerging Markets as model universes. All models use a 60 day horizon with 21 day half-life. Each fundamental factor was constructed in a separate model so that correlations across factors would be ignored. Tables 2 and 3 show the top ten fundamental factors for each index by percentage of variability explained. The value of the dollar was a significant driver of risk in all three indices. European macro factors also contributed to risk in the MSCI Europe and World indices, while U.S. macro factors affected risk in the S&P 500 index. 6

7 Tables 2a-c: Percent of Variability Explained by the Top Ten Fundamental Factors for the, S&P 500 and MSCI Europe Indices MSCI Europe Factor % Variability Explained Factor % Variability Explained S&P 100 CDS Rolling 26.9% Dollar Index Spot 30. Dollar Index Spot 23.2% Eurostat Industrial Production Index 29.7% Eurostat Industrial Production 22. Eurostat Producer Prices Index 27.8% S&P GSCI Crude Oil Index Spot 21. S&P GSCI Crude Oil Index Spot 24. Eurostat Producer Prices Index 21.1% S&P GSCI Industrial Metals Index Spot 23.1% US Credit Spread (Avg-30Y) 20.3% US Credit Spread (BAA-AAA) 22.6% S&P GSCI Industrial Metals Index Spot 19.8% Baltic Dry Index 21.9% US Credit Spread (BAA-AAA) 19.4% EU Economic Sentiment Indicator 15.6% Baltic Dry Index 17.4% Euro Credit Spread (BBB-AAA) 15.3% S&P GSCI Soybeans Index Spot 13.8% EU Confidence Indicator 15.1% Factor S&P 500 % Variability Explained S&P 100 CDS Rolling 28. Dollar Index Spot 18.7% S&P GSCI Crude Oil Index Spot 17.6% S&P GSCI Industrial Metals Index Spot 17.4% US Credit Spread (BAA-AAA) 16.9% US Credit Spread (Avg-30Y) 16.3% S&P GSCI Soybeans Index Spot 12.8% S&P CDS US High Yield Rolling 11. Baltic Dry Index 11.4% S&P CDS US Inv Grade Rolling 11.1% Source: Nomura Liquid Markets Analytics 7

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