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Risk Management in a Non- Transparent World: Perspective from a Fund of Hedge Funds

Risk Management in a Non- Transparent World: Perspective from a Fund of Hedge Funds. Gregory B. van Inwegen, PhD Managing Director Chief Investment Risk Officer Ivy Asset Management Corp Prepared for the Quant Congress 2008 New York, New York, USA July 9, 2008. Disclaimer

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Risk Management in a Non- Transparent World: Perspective from a Fund of Hedge Funds

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  1. Risk Management in a Non- Transparent World: Perspective from a Fund of Hedge Funds Gregory B. van Inwegen, PhD Managing Director Chief Investment Risk Officer Ivy Asset Management Corp Prepared for the Quant Congress 2008 New York, New York, USA July 9, 2008

  2. Disclaimer The opinions that I discuss are my own and not necessarily those of Ivy Asset Management Corp or BNY Mellon.

  3. Ivy Risk Management and Quantitative Research Team • Greg van Inwegen, PhD • Aleksey Matiychenko, CFA, CAIA, FRM • Eric Konigsberg, CAIA, FRM, PRM • Adam Tashman, PhD • David Cru , PhD Candidate

  4. Transparency • What is it? • What do you have? • What do I have?

  5. Simplified Version of Call with Hedge Fund • RM: I see you are overall 700% long/ 25% short. Do you allocate to Asia? • HF: We invest globally. • RM: Ok, Do you invest in equities? • HF: We invest in many security types. • RM: Thanks for your valuable insights. • HF: Sure, don’t hesitate to call again.

  6. Example Risk Report • Structured Finance Fund (July 2007) “Yesterday is dead and gone And tomorrow is out of sight Lord it’s bad to be alone Help me make it through the night”

  7. Example Risk Report Long/Short Equity- Jan 2008 “I beg your pardon We never promised you a rose garden A long with the sunshine There has got to be a little rain some time”

  8. Hedge Fund Due Diligence Visit • Insert Dilbert/Dogbert bad customer service cartoon.

  9. Quant Tools to Complement the Qualitative Approach • Various degrees of transparency • Adapted techniques. • Could be helpful, even with access to holdings.

  10. Aspects of a Hedge Fund of Fund • 15-30 HF managers per portfolio typical. • Multiple Strategies used. • Investments in 150 different HF managers. • Hedge Funds are lightly regulated (incidentally many have no risk officers).

  11. Hldgs Aggregate Exposure Historical Performance Data Types of Portfolio Data Type 1 10% Type 2 65% 98% Type 3 Economic/Market Data

  12. Problems in Addition to Lack of Transparency • Non Linear Factor Relationship • Non-Normality Returns • Complexity • Illiquidity • Diversity of Strategies

  13. Solutions to HF Challenges • Multi-Factor Risk Model & Stress Testing. • Non Normal Monte Carlo. • Measuring and Adjusting for Illiquidity. • Non-Normal Risk Budgeting. • Methods for Truncated Time Series.

  14. Data Type: Performance Data (Type 3) • Multi-Factor Models: Linear in parameters, Linear in Regressors • 13 Strategy Specific Models (eg Merger Arb, Convert Arb)

  15. Linear, LinearRisk and Return Contribution Contribution to Risk Contribution to Return Return Risk

  16. Data Type: Performance Data (Type 3) • Multi-Factor Models: Linear, Non Linear Regressor • If 2nd coefficient significant implies • Detecting a non-linear payout (eg from options, bond convexity) held by HF manager, or • Detecting a non-linear payout from dynamic beta timing of linear instruments by the HF manager.

  17. Linear Model, Non-Linear Regressor

  18. Data Type: Performance Data (Type 3)Non Linear Model, Linear Regressor (Tashman Frey) • A set of risk factors G determines the probability that the hedge fund is in a certain regime • Given the regime, a linear factor model relates hedge fund returns to a set of risk factors F Linear Factor Models Regime-Switching Component Prob(Regime 1): Regime 1: Prob(Regime 2): Regime 2: The expected return is the probability-weighted expected returns from each linear model:

  19. Data Type: Performance Data (Type 3)Non Linear Model, Linear Regressors Linear components Mixing componentblends linear pieces

  20. Data Type: Performance Data (Type 3) • Summary of Multi-Factor Models • Linear Model, Linear in Regressors (eg Fama/French) • Linear Model, Non-Linear in Regressors (Naik/Aggerwal) • Non Linear Model, Linear in Regressors (Tashman/Frey) • Non Linear Model, Non Linear in Regressors?

  21. Hldgs Aggregate Exposure Historical Performance Data Types of Portfolio Data Type 1 10% Type 2 65% 98% Type 3 Economic/Market Data

  22. Data Type 2: Sample Risk Report- Sector

  23. IVY Asset Management Corp. Type 2 Data: Aggregated Exposures: Provided (from risk reports), Sector Exposures

  24. Data Type 2: Sample Risk Report- Greeks

  25. IVY Asset Management Corp. Aggregated Exposures: Provided (from risk reports), Portfolio Level Greeks Portfolio Returnt = Delta x (Market Return)t + Gamma x .5 x (market return)t2 + Vega x (volatility change)t

  26. Liquidity Data from Risk Report Are these numbers realistic?

  27. “Forensic” analysis tool- Serial Correlation • For example, if rho>80%, this might suggest liquidity distribution table maybe optimistic. • Serial Correlation → Stale pricing → illiquidity? • Serial Correlation → Return Smoothing or Fraud detector?

  28. Adjusting Volatility for Autocorrelation Significant positive autocorrelation (e.g., momentum) or negative autocorrelation (e.g., mean reversion) of monthly returns prevalent among hedge fund strategies. The presence of autocorrelation can lead to under- or over-estimation of risk/return ratios (e.g., Sharpe Ratio). Adjusted volatility equation is as follows: • Where • Rt(q) is the q-period return • is the kth-order autocorrelation of Rt

  29. Tools for Black Boxes to Avoid Blowups

  30. Returns Based Multi-Factor Modeling as a Complement to Holdings Based Analysis • Basic Risk System Sector Data Misleading? • Nestle- Swiss Franc exposure? • Nokia- Finnish bet? • Caterpillar- US Industrial? • Royal Dutch- Dutch exposure? • Complexity makes holdings analysis difficult? • Holdings data not that useful if you know what you have but you have bad prices (stale) • Validation, Sanity Check? • Multiple Sector Classification- eg BARRA GE

  31. Caterpillar • Caterpillar, Inc. is a U.S.-based company that manufactures and sells: construction and mining equipment; diesel and natural gas engines; and industrial gas turbines. • If a hedge fund were to categorize their investment in Caterpillar solely as an exposure to a U.S. company, they would be ignoring the fact that the companies cash flows are dependent on countries worldwide, including some emerging markets. • A regression analysis of the stock’s return history against several global equity indices tells a different story (note, only significant factors are shown below): • The results above show that Caterpillar’s return’s are also dependent on Asia-Pacific markets, not just the U.S.

  32. Limitations of Multi-Factor Modeling • Spurious Results • Some meaningless factors can be correlated • Limited time series data

  33. Other Approaches:3rd party Aggregate Data • Vendors collect holdings data from HFs. • Commercial Returns-Based analytics

  34. Conclusion • Hedge FoF have an especially challenging issue regarding transparency of HF investments. • Qualitative due diligence is helpful. • Quantitative tools using performance data also useful. • Even in case where full transparency is available, returns based factor models helpful.

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