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Market Risk Management for Hedge Funds: Foundations of the Style and Implicit Value-at-Risk


Market Risk Management for Hedge Funds provides a clear understanding of the fundamentals of quantitative risk measurement, as well as covering the technical aspects of the Style Value-at-Risk and the Implicit Value-at-Risk measurements applied to hedge funds.

The book is divided into three parts. The first part explains the fundamentals of the Style and Implicit Value-at-Risk measurements as seen through the eyes of the alternative industry practitioner. It describes the effects of the ongoing institutionalisation of the hedge fund domain and examines one of the most important features of an absolute return industry. This section also addresses the issues of active and passive hedge fund indices, the failure of both approaches to provide a good representation of hedge funds, and finally provides a qualitative insight of the four dimensions of risk management for the hedge fund investor.

Part two is devoted to Style Value-at-Risk measurement, presenting the original model as well as out-of-the-sample back-testing. It also proposes a new parameterisation of the Style Model, addresses the issue of the annualisation of risk measurement for hedge funds, and illustrates a fundamental difference between traditional and alternative investments.

Part three presents the Best Choice Implicit Model by addressing the limits of the Style analysis and introducing the Best Choice Implicit Value-at-Risk. It also addresses the issue of hedge fund return cloning within the Best Choice Implicit Model framework, and details the Risk Budgeting approach that can be used with these types of models. Finally, it examines the forecasting power of Value-at-Risk exception monitoring, and provides some adjustments to Value-at-Risk that are particularly relevant during financing crises.

François Duc is head of the Risk Advisory Desk for alternative investments of UBP (Union Bancaire Privée), the second largest worldwide investor in hedge funds. Prior to joining UBP in October 2005, Francois was responsible for the quantitative analysis and risk management at Banque SYZ & Co. In addition, he has written articles in finance, statistics and general equilibrium theory for various publications and is co-editor of a book on a learning process. Francois did his PhD in Econometrics at Geneva University where he was Assistant Professor in Statistics.

Yann Schorderet works as a quantitative strategist at Banque Mirabaud & Cie. From June 2004 to June 2006, he was a member of both the Risk Advisory team and the Quantitative Team at UBP (Union Bancaire Privée). In 2003, he acted as a quantitative analyst in a start-up company specialised in funds of hedge funds. Prior to that, he was Assistant Professor in the Department of Econometrics of the University of Geneva and the Laboratoire d’Economie Appliquée. From 2001 to 2002, he carried out post-doctoral research at the University of California, San Diego. He holds a PhD in econometrics and statistics from the University of Geneva. Yann is a CFA charterholder.



1 Introduction

Part I Fundamentals for Style and Implicit Values-at-Risk

2 Ongoing Institutionalization

2.1 Hedge funds industry size and asset flows

2.2 Style distribution

2.3 2006-2007 structural developments

2.4 Are hedge funds becoming decent?

2.5 Funds of hedge funds persistence

3 Heterogeneity of Hedge Funds

3.1 Testing sample

3.2 Smoothing effect of a restrictive classification

3.3 Heterogeneity revealed through Modern Cluster Analysis

3.4 Appendix A: Indices sample

4 Active and Passive Hedge Fund Indices

4.1 Illusions fostered by active hedge fund indices

4.2 Passive indices and the illusion of being clones

4.3 Conclusion

5 The Four Dimensions of Risk Management for Hedge Funds

5.1 Operational and structural risk

5.2 Risk control

5.3 Delegation risk

5.4 Direct investment risk

5.5 Conclusion

5.6 Appendix B: Risks embedded with some classical alternative strategies

5.7 Appendix C: Other common risks to hedge funds

Part II Style Value-at-Risk

6 The Original Style VaR Revisited 77

6.1 The Multi-Index Model

6.2 The Style Value-at-Risk

6.3 Backtesting revisited

7 The New Style Model

7.1 Extreme Value Theory

7.2 Risk consolidation

7.3 The New Style Model

7.4 Appendix D: Algorithms for the elemental percentile method

7.5 Appendix E: Copulas

8 Annualization Problem

8.1 Annualization of the main statistical indicators assuming i.i.d.

8.2 Annualization of Value-at-Risk assuming i.i.d.

8.3 Annualization without assuming i.i.d.

8.4 Applications to the Style Value-at-Risk

8.5 Appendix F: annualization of excess kurtosis  

8.6 Appendix G: Drost and Nijman Theorem

Part III Implicit Value-at-Risk

9 The Best Choice Implicit Value-at-Risk

9.1 Alternative style analysis and BCI Model

9.2 Theoretical framework of BCIM

9.3 Best Choice Implicit VaR

9.4 Empirical Tests

10 BCI Model and Hedge Fund Clones

10.1 Ten-Factor Model

10.2 Non-Linear Model

11 Risk Budgeting

11.1 Value-at-Risk of a multi-managers portfolio

11.2 Risk decomposition: 'before and after' attribution

11.3 Risk decomposition: closed form attribution

12 Value-at-Risk Monitoring

12.1 Analyzing graveyards and hedge funds demise

12.2 The probit model

12.3 Empirical evidence

12.4 Implications for portfolio management

13 Beyond Value-at-Risk

13.1 2007–2008 liquidity crisis and hedge funds

13.2 Mechanical stress test

13.3 Liquidity-adjusted Value-at-Risk

13.4 Limit of liquidity-adjusted Value-at-Risk and liquidity scenario