Diversification is the most rudimentary, but at the same time one of the key ele- ments of portfolio construction and risk management in finance. Modern portfolio theory21(MPT) states that correlation properties are the key to efficient diversifica- tion in an investment portfolio. In the investment practice the calculation of efficient frontiers is the most important application of MPT. Numerous studies22 (see also Chapter 4) have shown that due to low correlations to traditional assets, AIS investments offer attractive possibilities for diversification and efficiency enhancement in an investor’s global portfolio. At the same time, the performance and correlation properties of AIS differ widely across the strategy sectors.23
A note of caution is necessary upfront: quantitative optimization for AIS port- folios can be quite useful in determining the scope for improvement from adding AIS to a global traditional balanced (equity and bond) portfolio, however, it shows its limitations if used for determining the right AIS sector allocation within a multi-manager AIS portfolio. The results of Markowitz-type optimizations are
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very sensitive to the return and risk assumption used in the calculation. The avail- ability and reliability of AIS performance data leaves much to be desired, as discussed in detail in Chapters 4 and 7. Data is available only for a limited time frame and is biased due to the specific market circumstances in the 1990s (bull equity markets). The performance of the different strategy sectors is very hetero- geneous and generally far from stationary, i.e. the mean return, standard deviation and the correlations, all important inputs to portfolio optimization programs, vary substantially with respect to chosen time periods, market environments and indi- vidual managers. Taking average performance numbers over a time period of a few years as representative for the strategy sectors in the portfolio optimization does not account sufficiently for this variability. Performance and correlation properties between the different strategies remain an important consideration in setting up an AIS portfolio, but experienced AIS portfolio managers do not apply quantitative portfolio optimization on a sole basis, rather they combine it with qualitative considerations.
The diversification of equity risk in the traditional portfolio is one of the main motivations for investing in AIS. But for this very purpose, certain combinations of strategies are better suited than others. For example, Relative Value and Managed Futures strategies show generally lower correlations to equity markets than most Opportunistic strategies. Chapter 4 introduced the concept of condi- tional correlations, which provides a powerful tool for examining the effectiveness of portfolio diversification.
Figure 5.11 shows an example of two different sets of strategies (encircled) and their past correlation properties conditional to equity market directions(compare with Figures 4.4 and 4.5). The figure illustrates clearly that the second set pro- vides better diversification in periods of negative equity performance than the first. The first portfolio consisting of Long/Short Equity, Global Macro, Event Driven and Distressed Securities is biased towards an environment of rising equity markets and shows weaknesses during periods of negative equity market perform- ance. In contrast, the combination of Managed Futures, Equity Market Timing and Relative Value strategies (Convertible Arbitrage, Fixed Income Arbitrage, Equity Market Neutral) shows strengths in various equity market environments.
In periods of falling equity markets, Managed Futures tend to perform well24and
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Global Macro
–1.0 –0.8 –0.6 –0.4 –0.2 0.0 0.6 1.0
–0.8 –0.6 –0.4 –0.2 0.2 0.4 0.6 0.8
–1.0 0.0 1.0
Correlation to S&P500 in bad months
Correlation to S&P500 in good months
Equity Market Timer
Long/Short Equity
Event Driven Reg D
Distressed Securities Currencies0.2
Equity Market Neutral
Commodity Long Futures Discretionary
Short Seller Futures Passive
Futures Systematic
Active
Global Macro
–1.0 –0.8 –0.6 –0.4 –0.2 0.0 0.4 0.6 0.8 1.0
–0.8 –0.6 –0.4 –0.2 0.2 0.4 0.6 0.8
–1.0 0.0 1.0
Correlation to S&P500 in bad months
Correlation to S&P500 in good months
Equity Market Timer
Long/Short Equity
Event Driven Reg D
Distressed Securities Relative
Value 0.2 Currencies
Equity Market Neutral
Commodity Long Futures Discretionary
Short Seller Futures Passive
Futures Systematic
Active
Relative Value
0.4 0.8
(b) (a)
FIGURE 5.11■Testing diversification with conditional correlation (compare with Figures 4.4 and 4.5) (a) Example for a portfolio with strong equity correlations in falling markets
(b) Example for a diversified portfolio
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Equity Market Timing strategies are almost uncorrelated to equity markets. In periods of rising equity markets, Equity Market timers show strong performance, while Managed Futures are uncorrelated. Relative Value strategies generally dis- play low correlation to equity market directions in ‘normal periods’ but tend to be highly correlated in times of market turbulence (not shown in Figure 5.11).
Another example is the following: A combination of Convertible Arbitrage and Risk Arbitrage can provide favourable correlation properties conditional to equity market volatility. Convertible Arbitrage strategies show above average performance during periods of increasing equity market volatility, when Risk Arbitrage strategies struggle. Low volatility periods bear inverse performance behaviour. Risk Arbitrage is attractive, Convertible Arbitrage less so.
Another important analysis refers to the returns and correlations conditional on different bond market performance. Generally, a systematic study of condi- tional returns and correlations in various market environments (e.g. with respect to equities, bonds, FX) can lead the AIS investor to similar insights as multi-factor models of returns.
Statistical analysis and correlation studies are only one way to approach the subject of AIS portfolio diversification. Another approach, which aims at supple- menting the results of statistical studies on past performance data, is based on fundamental economic reasoning and looks more closely at the economic reasons and sources of AIS returns. Understanding the return generation process for each strategy can help to reach efficient portfolio diversification. As discussed in Chapters 2 and 3, individual strategies can have very different performance driv- ers, as they represent different economic functions in financial markets. The following list builds on the discussion in Chapter 2 and summarizes the classifica- tion of the AIS in terms of economic functions:
■ Capital formation (providing capital to companies): Equity Market Timing, Long/Short Equity. The primary purpose of equity markets is the formation and efficient distribution of capital needed by companies to foster innovation and economic progress. Investors provide the needed capital and ensure its efficient allocation to the most promising businesses.
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■ Risk transfer(taking risk from commercial hedgers): Managed Futures. The investor provides commercial hedgers with the necessary liquidity to perform their hedging activities. Similar to insurance companies, they are compensated with a risk premium.
■ Relative Value Arbitrage(making markets more efficient): Convertible
Arbitrage, Risk Arbitrage, Fixed Income Arbitrageand Equity Market Neutral.
Arbitrageurs25take advantage of relative mispricings or differences in credit, liquidity or other risk factors of different but generally closely related securities. This provides financial markets with a higher degree of efficiency and a less erratic price formation process.
■ Providing liquidity(taking positions in less liquid markets and instruments):
Distressed Securities, Regulation D, some Fixed Income Arbitrage. Managers invest in less liquid instruments and earn a liquidity premium, thus providing a market for instruments less frequently traded.
■ The economic function for Global Macro, Short Sellingand some Long/Short Equitystrategies is less clear. The returns of these are based on the manager’s skills in forecasting stock price developments and acting quickly rather than earning particular risk premiums. Usually, these strategies involve a higher degree of speculation.
Low statistical correlations to equity and other markets as well as between differ- ent AIS sectors usually find their theoretical explanation in different economic functions of the corresponding strategies. So for the example just discussed: the set of Equity Market Timing (economic function: capital formation), Futures (eco- nomic function: risk transfer) and Relative Value (economic function: market efficiency) contains strategies with very different return generation processes.
Next to statistical correlation features, the AIS allocator should consider the eco- nomic sources of returns for the purpose of AIS portfolio diversification.
Notes
1. See the article by L. Rahl, ‘Risk Budgeting: The Next Step of the Risk
Management Journey – A Veteran’s Perspective’, in L. Rahl (ed.) Risk Budgeting: A New Approach to Investing.
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5 ■Risk in Alternative Investment Strategies
2. See also the study ‘Hedge Funds and Financial Markets Dynamics’, by B. Eichengreen and D. Mathieson.
3. This risk is sometimes classified as operational risk, but in our context it should be seen as a risk type on its own.
4. See also report issued by a group of five Hedge fund managers, ‘Sound Practices for Hedge Fund Managers’, section ‘Recommendations: Risk Monitoring’.
5. Personal communication with M. Perkins, MKP. See also ‘Prime Brokers Can Make and Break Hedge Funds’ in Pension & Investments, September 3, 2001.
6. For more recent developments in the relationship between Hedge funds and their prime brokerage counter-parties, see the article by Patel and Navroz,
‘Courting the Hedge Funds’ in Risk Magazine, November 2001.
7. See the article ‘Hedge Fund Disasters: Avoiding the Next Catastrophe’ by D. Kramer in Alternative Investment Quarterly, October 2001.
8. See Chris Clair, ‘Hedge Funds Suffer Without a Benchmark’, Pension &
Investments, June 11, 2001.
9. See also the work by W. Fung and D. Hsieh, ‘Benchmarks of Hedge Fund Performance: Information Content and Measurement Biases’.
10. Dacorogna et al. in ‘Effective Return, Risk Aversion and Drawdowns’ in Physica A, January 2001, provide an excellent presentation of different performance measurements.
11. See the articles ‘Life at Sharpe’s end’ and ‘Measure for Measure’ by H. Till in Risk and Reward, September and October 2001.
12. A study ‘Do Hedge Funds Hedge?’ by C. Asness et al. examines the effect of these liquidity-induced lags on correlation and performance properties of Hedge funds.
13. Capital Market Risk Advisors, ‘NAV / Fair Value Practices Survey Results’, July 2001, available on http://www.cmra.com.
14. See B. Liang, ‘Hedge Funds: On the Performance of Hedge Funds’ in Financial Analysts Journal, July 1999.
15. See the discussion in ‘TSS(II)-Tactical Style Selection: Integrating Hedge Funds into the Asset Allocation Framework’ by Crossborder Capital, published by Hedge Fund Research, August 2000.
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16. See the article ‘Convertible Arb Funds turn to Default Swaps’ by C. Schenk in Risk Magazine, July 2001.
17. See F. Fabozzi in Fixed Income Analysis(Chapter 11, 2) for more details on OAS models.
18. CMRA stands for ‘Capital Markets Risk Advisors’; see article by G. Polyn,
‘Value-at-Risk for Merger Arbs’ in Risk Magazine, April 2001, p.6.
19. See the publications by B. LeBaron, ‘Forecast Improvements using a Volatility Index’ and ‘Some Relation between Volatility and Serial Correlations in Stock Market Returns’.
20. See the work by Pictet et al., ‘Real Time Trading Models for Foreign Exchange Rates’, where possible target functions for model parameter optimization are discussed.
21. Modern Portfolio Theory (MPT) is a topic in almost all finance textbooks and university classes. For an introduction into MPT refer to Investment Analysis and Portfolio Managementby F. Reilly and K. Brown.
22. See the article ‘The Benefits of Hedge Funds’ by T. Schneeweiss and references therein.
23. For a discussion of diversification within a multi-strategy portfolio, see J. Park and J. Strum, ‘Fund of Fund Diversification: How Much is Enough?, The Journal of Alternative Investments, Winter 1999. See also R. McFall Lamm, ‘Portfolios of Alternative Assets: Why not 100% Hedge Funds?’, The Journal of Investing, Winter 1999, pp.87–97.
24. This statement applies when interest rates remain constant or fall. CTA actually struggles when interest rates rise. See the discussion in Chapter 4 and also the work by M. Caglayan and F. Edwards, ‘Hedge Fund and Commodity Fund Investment Styles in Bull and Bear Markets’. The authors of this article found that CTAs have higher returns than Hedge funds in bear markets and generally have a negative correlation with equity returns in periods of falling markets.
25. I am not referring to arbitrage in the strictest meaning of the word here (which is ‘generating a profit without risk’): With ‘Relative Value Arbitrage’ I generally mean ‘buying relatively undervalued securities and selling overvalued securities’. There is a risk involved, specifically, the risk that the undervalued securities become even cheaper and the overvalued ones more expensive.
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C H A P T E R 6
Tools and Principles of Modern Financial Risk Management
Although there is no unique and widely accepted definition of risk in financial markets, on a very general level, risk is the result of holding an investment (expo- sure) combined with the uncertainty about future price developments (with risk factor variability). Investment decision makers can control exposure, while uncer- tainty is by nature uncontrollable. Risk measurement starts with the analysis of these two components. Exposure analysis is the categorization of investment posi- tions by risk factors, and uncertainty is often described by statistical estimates of future price variability, i.e. volatility (as measured, for example, by the standard deviation of historical returns).
It is acknowledged among risk professionals that the widespread perception of risk and volatility as equivalents leads to a very incomplete picture of financial risk. Statistical measures of volatility most often do not capture all the risks an investor is facing (e.g. extreme market moves). Therefore, the emergence of vari- ous risk quantification tools (such as VaR) notwithstanding, qualitative considerations remain important for the complete assessment of risk, as some risk factors defy accurate quantification. This is particularly true for AIS, where the wide range of investment instruments and trading techniques exposes the investor to more numerous and more complex risks compared to traditional investments (see Chapter 5) and therefore makes risk quantification all the more challenging.
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The foundation of effective risk management is a general risk policy outlining what types of risk are acceptable and which are to be avoided. The policy must be supported by appropriate tools that allow for monitoring and control and, impor- tantly, by the actions and attitudes of key personnel. In the end, risk management must be more than just risk quantification or risk measurement; it must involve spe- cific actions, which directly influence the investment decision-making process and which ensure that corrective action is taken when risk exceeds permitted levels.
This chapter aims at introducing the reader to the state-of-the-art financial risk management tools and principles, including a discussion of their strengths and shortcomings. It cannot claim complete coverage of this extensive subject.
Numerous articles, books and encyclopaedias have been published over the years1 and it is almost impossible to cover them all. The interested reader is advised to read the references provided. They are, naturally, selective.2