Cyclicality in Risk and Return. ICPM Webinar May 23, 2017 Hens Steehouwer

Transcription

1 Cyclicality in Risk and Return ICPM Webinar May 23, 2017 Hens Steehouwer

2 Content Part I Introduction to cyclicality Research questions Part II Research methodology Cycle estimates Risk and return Conclusions 2

3 The essence of our problem A more realistic assessment of what might (rather than what will) happen in the future contributes to people and organizations becoming more successful in achieving their objectives US equities total return index in USD (log scale) Decisions Uncertainty Objectives 3

4 A better understanding of how economies and financial markets move up and down contributes to better decisions and more likely achievement of objectives. 4

5 Dampened exogenous shocks

6 or endogenous cyclicality? In this view, fluctuations are not (only) caused by exogenous shocks, but the fluctuations are (also) endogenously generated from within economies and financial markets. In such self-generating cycles each phase is caused by the course of events that took place during the previous phase. 6

7 Short term cycles Seasonality 0.6% 0.6% Spring Summer Autumn Winter 0.4% 0.4% 0.2% 0.2% 0.0% 0.0% -0.2% -0.2% -0.4% -0.4% -0.6% Mar-12 Mar-13 Mar-14 Mar-15 Mar-16 Mar -0.6% Seasonal component of 1-Month Percent Change of Consumer Price Index - All Urban Consumers - U.S. city average - All items. Source: Bureau of Labor Statistics Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Average

8 Medium term cycles Business cycles Slowdown Expansion a type of fluctuations found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals which merge into the expansion phase of the next cycle; this sequence of changes is recurrent but not periodic; in duration business cycles vary from more than 1 year to 10 or 12 years Burns and Mitchell (1946) 8 Contraction Recovery Behavioral as well as structural drivers KITCHIN inventory cycle: Average length of 3 to 5 years, named after Kitchin (1923). JUGLAR investment cycle: Average length of 7 to 11 years, named after Juglar (1862).

9 Long term cycles KONDRATIEFF innovation cycle: named after Kondratieff (1926) but first reported by Van Gelderen (1913) and made famous by Schumpeter (1939). Length of some 45 to 60 years. Driven by a product life cycle at large, identified with basic innovations or technological styles. Trough Peak Trough Basic Innovation Steam transport Steel and electricity Cars and assembly lines Microelectronics and biotechnology ?? ICT? KUZNETS building cycle: as found by Kuznets (1930) with a length of 15 to 25 years. Only US pre-1914, probably not an endogenous cycle or the Financial Cycle? 9

10 The financial cycle The broad concept of the financial cycle encapsulates joint fluctuations in a wide set of financial variables. Financial cycles are characterized by financial booms and busts that can lead to serious financial and macroeconomic strains. Drehmann et al. (2012) find an average length of 16 to 20 years and dominant information to be contained in credit to GDP ratios and real house prices. Much more debated than business cycles, both in terms of existence as such, measurement methodology and policy implications Stylized facts: under construction Central banks: generally accepted and important for macro prudential policy (counter cyclical buffer in Basel and Solvency linked to financial cycle), debate about definitions, measurement and so forth Macroeconomists: financial sector not an important factor in their models Investors: not a central theme but shouldn t it be?? 10

11 The financial instability hypothesis Minsky, H. (1982), Can it happen again, Essays on Instability and Finance Ponzi financing Speculative financing Hyman Minsky ( ) Hedge financing 11

12 Research questions Focus on business cycle and financial cycle Use a new dataset by Jordà, Schularick and Taylor (2017) Annual data series x 17 countries = 425 series Added to JST data realized volatility series and artificial long term government bond indices Two research questions 1. What do estimates of these cycles look like and how sensitive are these for input data, cycle definition and extraction methodology? 2. Are these cycles related to asset class risk and return, investigated for United States bonds, house prices and stocks? 12

13 Content Part I Introduction to cyclicality Research questions Part II Research methodology Cycle estimates Risk and return Conclusions 13

14 Analyzing cyclicality Spectral analysis: frequencies and variance 0 Frequency Time Frequency Period length Variance (%) % % % % % 100% 80% 60% + Integrated spectral density 40% 20% Spectral density 0% Spectrum Integrated spectrum

15 Analyzing cyclicality Spectral analysis: correlations, leads and lags Correlation 0% = Coherence 100% + Lead

16 Two step cycle estimation As in Lee and Steehouwer (2012) Example step 1: financial cycle between 30 and 16 years from US debt to GDP ratio Step 1: isolate all fluctuations with a certain period length / frequency Frequency domain band-pass filter Same filter for all series Example step 2: business cycle component of 300 macroeconomic and financial market variables Step 2: factor to obtain cycle estimate Joint behaviour through underlying factors Standardize data to focus on patterns rather than levels and volatilities st PCA

17 Business cycle: estimates Similar, but also sensitive to input data and methodology OECD CLI (Nov16) OF BCI (Dec16) JST_16Y_2Y (data 1,2,3) JST input data 347 series (Bretton Woods) Levels and annual log returns Filter: 16Y-2Y Reference series OECD Composite Leading Indicator (CLI) Ortec Finance Business Cycle Indicator (OF BCI) 17

18 Business cycle: dynamics Dominant average cycle length 8 years Spectral densities 100% 80% % 40% 20% Integrated spectral densities OECD CLI (Nov16) OF BCI (Dec16) JST_16Y_2Y (data 1,2,3) 0% Cycles per year OECD CLI (Nov16) OF BCI (Dec16) JST_16Y_2Y (data 1,2,3) 18 Peak freq. Period (years) OECD CLI OF BCI JST_16Y_2Y (data 1,2,3) Average Coh. OECD CLI Lead OECD CLI OF BCI 97% 0.5 JST_16Y_2Y (data 1,2,3) 95% -0.2

19 Financial cycle: data and filter choices JST input data Levels and log indices 296 series in 4 groups (core financial cycle, wider credit, wider financial and wider macro) Reference series US estimate from Drehmann et al. (2012), starting in 1970 and updated until Q Key findings and preferred estimation settings Prefer 30Y-16Y filter over 30Y-8Y (no overlap business cycle and more common variance) Prefer real input data over nominal (more robust for input data and more common variance) Prefer debt to GDP and real house price data (consistent with Drehmann et al. (2012), fairly robust for filter pass-band and for nominal or real input data) which means that effectively we estimate the financial cycle on 25 input series 19

20 Financial cycle: estimate and dynamics Average cycle length around 25 years JST_30Y_16Y (data 1) FC_BIS (Drehmann) rescaled Peak to peak (years) Avg: 24 Peak Trough Trough to trough (years) Avg: Correlation on overlapping sample: 0.70

21 Related to asset class risk and return? Yes! US Government bonds US House prices US Stocks US Government bond volatility US Stock volatility 21 Coherence Lead (years) Correlation JST_16Y_2Y lgbondidx_usa gov. bonds 88% 2.5 9% hpnom_usa house prices 96% % US stocks_usa 72% % -US- bond NGLR_USA_RV volatility 89% % -US- stock EQCI_USA_RV volatility 92% % At cycle peak frequency Cycles per year

22 Related to asset class risk and return? Yes! US Government bonds US House prices US Stocks US Government bond volatility US Stock volatility 22 Coherence Lead (years) Correlation JST_30Y_16Y (data 1) lgbondidx_usa gov. bonds 79% % hpnom_usa house prices 98% % stocks_usa 90% % US NGLR_USA_RV bond volatility 81% % US EQCI_USA_RV stock volatility 99% % At cycle peak frequency Cycles per 8 years

23 Content Part I Introduction to cyclicality Research questions Part II Research methodology Cycle estimates Risk and return Conclusions 23

24 Conclusions A better understanding of how economies and financial markets move up and down contributes to better decisions and more likely achievement of objectives. Estimates of the business cycle and the financial cycle are sensitive to data and methodology. However, not to the extent that this dramatically affects (alleged) stylized facts. Medium term asset class risk and return are related to the business cycle. Long term asset class risk and return appear related to the financial cycle as well. A place for the financial cycle in long term investment decision making? More research on the financial cycle is needed Collection of stylized facts in general And specifically about the relation to asset class risk and return 24

25 References Burns, A.F. and W.C. Mitchell (1946), Measuring business cycles, New York: National Bureau of Economic Research, Ch. 11. Drehmann, M., C. Borio and K. Tsatsaronis (2012), Characterising the financial cycle: don t lose sight of the medium term!, BIS Working Papers No 380. Van Gelderen, J. (pseudonym J. Fedder) (1913), Springvloed. Beschouwingen over industriële ontwikkelingen en prijsbeweging, De Nieuwe Tijd, , , , Reprinted in Freeman, C. (editor), Long wave theory, E. Elgar, Cheltenham, Jordà, O., M. Schularick and A.M. Taylor (2017), Macrofinancial History and the New Business Cycle Facts NBER Macroeconomics Annual 2016, volume 31, edited by Martin Eichenbaum and Jonathan A. Parker. Chicago: University of Chicago Press. Juglar, C. (1862), Des crises commerciales et de leur retour périodique en France, en Angleterre et aux Etats-Units, Guillamin, Paris. Lee, K. and H. Steehouwer (2012), A Zero Phase Shift Band Pass Filter, Paper presented at EEA-ESEM conference, August Kitchin, J. (1923), Cycles and trends in economic factors, Review of Economic Studies, Kondratieff, N.D. (1926), Die Lange Wellen der Konjunktur, Archiv für Sozialwissenschaft und Sozialpolitik, LVI, Kuznets, S.S. (1930), Secular movements in production and prices, Boston and New York: Houghton Mifflin. Minsky, H. (1982), Can it happen again, Essays on Instability and Finance Schumpeter, J.A. (1939), Business Cycles: A theoretical, historical and statistical analysis of the capitalist process, McGraw-Hill, NY. Steehouwer, H. (2005), Macroeconomic Scenarios and Reality. A Frequency Domain Approach for Analyzing Historical Time Series and Generating Scenarios for the Future, PhD Thesis, Free University of Amsterdam (free download from 25

26 Contact Hens Steehouwer, PhD Head of Research Boompjes 40, 3011 XB Rotterdam Rotterdam Ortec Finance bv Boompjes XB Rotterdam The Netherlands Tel. +31 (0) Amsterdam Ortec Finance bv Naritaweg BP Amsterdam The Netherlands Tel. +31 (0) London Ortec Finance Ltd Suite 9.10, City Tower 40 Basinghall Street London,EC2V 5DE United Kingdom Tel. +44 (0) Pfäffikon Ortec Finance AG Poststrasse Pfäffikon SZ Switzerland Tel. +41 (0) Toronto Ortec Finance Canada Inc 250 University Avenue Toronto, ON M5H 3E5 Canada Tel. +1 (0)

27 Jordà, Schularick and Taylor (JST) dataset Jordà, Schularick and Taylor (2017) - release 2 November 2016 Dataset Variables included Real Nominal GDP (local currency), Real GDP per capita (PPP), Real GDP Economy per capita (index, 2005=100), Real Consumption per capita (index, 2006=100), Investment-to-GDP Ratio, Population International Current Account (nominal, local currency), Imports (nominal, local currency), Exports (nominal, local currency), USD Exchange Rate (local currency/usd) Government Government Revenue (nominal, local currency), Government Expenditure (nominal, local currency), Public Debt-to-GDP Ratio Money, Narrow Money (nominal, local currency), Broad Money (nominal, local Prices & currency), Short-term Interest Rate (nominal, percent per year), Longterm Interest Rates (nominal, percent per year), Consumer Prices Interest Rates (index, 1990=100) Credit Data Total Loans to Non-financial Private Sector (nominal, local currency), Mortgage Loans to Non-financial Private Sector (nominal, local currency), Total Loans to Households (nominal, local currency), Total Loans to Business (nominal, local currency) Asset House Prices (index, 1990=100), Stock Prices (index, 1990=100) Prices Crisis Dates Systemic Financial Crisis (0-1 dummy) Annual data countries: AUS, BEL, CAN, CHE, DEU, DNK, ESP, FIN, FRA, GBR, ITA, JPN, NLD, NOR, PRT, SWE and USA Added: monthly Realized Volatility (RV) series for stocks and long interest rates Added: artificial long term government bond indices based on the long term interest rate data and assuming a duration of 5: R t r t-1 5 x (r t r t-1 ) Nominal series in local currency in real terms through division by corresponding consumer price index 27

28 Financial cycle: JST input data Levels and log indices 296 series divided into 4 broad groups 28 JST data for financial cycle estimates 1. core financial cycle debtgdp Public debt-to-gdp ratio hpnom House prices (nominal index, 1990=100) 2. wider credit tloans Total loans to non-financial private sector (nominal, local currency) tmort Mortgage loans to non-financial private sector (nominal, local curre thh Total loans to households (nominal, local currency) tbus Total loans to business (nominal, local currency) 3. wider financial narrowm Narrow money (nominal, local currency) money Broad money (nominal, local currency) stir Short-term interest rate (nominal, percent per year) ltrate Long-term interest rate (nominal, percent per year) stocks Stock prices (nominal index) 4. wider macro rgdppc Real GDP per capita (index, 2005=100) rconpc Real consumption per capita (index, 2006=100) iy Investment-to-GDP ratio cpi Consumer prices (index, 1990=100) ca Current account (nominal, local currency) imports Imports (nominal, local currency) exports Exports (nominal, local currency) revenue Government revenues (nominal, local currency) expenditure Government expenditure (nominal, local currency) xrusd USD exchange rate (local currency/usd) 25 series

29 Financial cycle estimate: data and filter choices Variance factor 1 Difference JST_30Y_8Y (data 1) 29% JST_30Y_8Y (data 1,2) 39% JST_30Y_8Y (data 1,2,3) 22% JST_30Y_8Y (data 1,2,3,4) 24% JST_30Y_16Y (data 1) 34% 5% JST_30Y_16Y (data 1,2) 43% 4% JST_30Y_16Y (data 1,2,3) 27% 5% JST_30Y_16Y (data 1,2,3,4) 29% 5% -3 JST_30Y_8Y (data 1) JST_30Y_16Y (data 1) Prefer 30Y-16Y filter over 30Y-8Y filter Avoid overlap with business cycle estimates More common variance across input series (+5%) Prefer real input data over nominal input data More robust for input data More common variance across input series (+5%) 29 Prefer data 1 (debt to GDP and real house prices) Consistent with Drehmann et al. (2012) Fairly robust for filter pass-band Fairly robust for nominal or real input data

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