Market Risk Management for Hedge Funds: Foundations of the Style and Implicit Value-at-Risk
The book begins by analysing the current state of the hedge fund industry - at the ongoing institutionalisation of the market, and at its latest developments. It then moves on to examine the range of risks, risk controls, and risk management strategies currently employed by practitioners, and focuses on particular risks embedded in the more classic investment strategies such as Long/Short, Convertible Arbitrage, Fixed Income Arbitrage, Short selling and risk arbitrage. Addressed along side these are other risks common to hedge funds, including liquidity risk, leverage risk and counterparty risk.
The book then moves on to examine more closely two models which provide the underpinning for market risk management in investment today - Style Value-at-Risk and Implicit Value-at-Risk. As well as full quantitative analysis and backtesting of each methodology, the authors go on to propose a new style model for style and implicit Var, complete with analysis, real life examples and backtesting. The authors then go on to discuss annualisation issues and risk return before moving on to propose a new model based on the authors own Best Choice Implicit VaR approach, incorporating quantitative analysis, market results and backtesting and also its potential for new hedge fund clone products.
This book is the only guide to VaR for Hedge Funds and will prove to be an invaluable resource as we embark into an era of increasing volatility and uncertainty.
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
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
5.6 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
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.
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