Hedge Fund Modelling and Analysis Using Excel and VBA: (The Wiley Finance Series)

Co-authored by two respected authorities on hedge funds and asset management, this implementation-oriented guide shows you how to employ a range of the most commonly used analysis tools and techniques both in industry and academia, for understanding, identifying and managing risk as well as for quantifying return factors across several key investment strategies. The book is also suitable for use as a core textbook for specialised graduate level courses in hedge funds and alternative investments. The book provides hands-on coverage of the visual and theoretical methods for measuring and modelling hedge fund performance with an emphasis on risk-adjusted performance metrics and techniques. A range of sophisticated risk analysis models and risk management strategies are also described in detail. Throughout, coverage is supplemented with helpful skill building exercises and worked examples in Excel and VBA. The book's dedicated website, www.darbyshirehampton.com provides Excel spreadsheets and VBA source code which can be freely downloaded and also features links to other relevant and useful resources. A comprehensive course in hedge fund modelling and analysis, this book arms you with the knowledge and tools required to effectively manage your risks and to optimise the return profile of your investment style.

Table of Contents:
Preface xi 1 The Hedge Fund Industry 1 1.1 What Are Hedge Funds? 1 1.2 The Structure of a Hedge Fund 4 1.2.1 Fund Administrators 5 1.2.2 Prime Brokers 5 1.2.3 Custodian, Auditors and Legal 6 1.3 The Global Hedge Fund Industry 7 1.3.1 North America 8 1.3.2 Europe 10 1.3.3 Asia 11 1.4 Specialist Investment Techniques 12 1.4.1 Short Selling 12 1.4.2 Leverage 14 1.4.3 Liquidity 15 1.5 New Developments for Hedge Funds 16 1.5.1 UCITS III Hedge Funds 16 1.5.2 The European Passport 19 1.5.3 Restrictions on Short Selling 20 2 Major Hedge Fund Strategies 23 2.1 Single- and Multi-Strategy Hedge Funds 23 2.2 Fund of Hedge Funds 25 2.3 Hedge Fund Strategies 27 2.3.1 Tactical Strategies 28 2.3.1.1 Global Macro 28 2.3.1.2 Managed Futures 31 2.3.1.3 Long/Short Equity 36 2.3.1.4 Pairs Trading 38 2.3.2 Event-Driven 42 2.3.2.1 Distressed Securities 42 2.3.2.2 Merger Arbitrage 46 2.3.3 Relative Value 49 2.3.3.1 Equity Market Neutral 49 2.3.3.2 Convertible Arbitrage 50 2.3.3.3 Fixed Income Arbitrage 54 2.3.3.3.1 Capital Structure Arbitrage 56 2.3.3.3.2 Swap-Spread Arbitrage 57 2.3.3.3.3 Yield Curve Arbitrage 58 3 Hedge Fund Data Sources 61 3.1 Hedge Fund Databases 61 3.2 Major Hedge Fund Indices 65 3.2.1 Non-investable and Investable Indices 66 3.2.2 Dow Jones Credit Suisse Hedge Fund Indexes 68 3.2.2.1 Liquid Alternative Betas 70 3.2.3 Hedge Fund Research 73 3.2.4 HedgeFund.net 77 3.2.5 FTSE Hedge 77 3.2.5.1 FTSE Hedge Momentum Index 78 3.2.6 Greenwich Alternative Investments 79 3.2.6.1 GAI Investable Indices 80 3.2.7 Morningstar Alternative Investment Center 83 3.2.7.1 MSCI Hedge Fund Classification Standard 83 3.2.7.2 MSCI Investable Indices 85 3.2.8 EDHEC Risk and Asset Management Research Centre (www.edhec-risk.com) 86 3.3 Database and Index Biases 88 3.3.1 Survivorship Bias 89 3.3.2 Instant History Bias 90 3.4 Benchmarking 91 3.4.1 Tracking Error 92 Appendix A: Weighting Schemes 95 4 Statistical Analysis 99 4.1 Basic Performance Plots 99 4.1.1 Value Added Monthly Index 99 4.1.2 Histograms 102 4.2 Probability Distributions 105 4.2.1 Populations and Samples 106 4.3 Probability Density Function 107 4.4 Cumulative Distribution Function 108 4.5 The Normal Distribution 109 4.5.1 Standard Normal Distribution 110 4.6 Visual Tests for Normality 111 4.6.1 Inspection 111 4.6.2 Normal Q-Q Plot 112 4.7 Moments of a Distribution 114 4.7.1 Mean and Standard Deviation 114 4.7.2 Skewness 117 4.7.3 Excess Kurtosis 119 4.7.4 Data Analysis Tool: Descriptive Statistics 120 4.8 Geometric Brownian Motion 122 4.8.1 Uniform Random Numbers 125 4.9 Covariance and Correlation 126 4.10 Regression Analysis 131 4.10.1 Ordinary Least Squares 131 4.10.1.1 Coefficient of Determination 133 4.10.1.2 Residual Plots 134 4.10.1.3 Jarque–Bera Normality Test 135 4.10.1.4 Data Analysis Tool: Regression 138 4.11 Portfolio Theory 142 4.11.1 Mean–Variance Analysis 142 4.11.2 Solver: Portfolio Optimisation 145 4.11.3 Efficient Portfolios 148 5 Risk-Adjusted Return Metrics 151 5.1 The Intuition behind Risk-Adjusted Returns 152 5.1.1 Risk-Adjusted Returns 154 5.2 Common Risk-Adjusted Performance Ratios 157 5.2.1 The Sharpe Ratio 160 5.2.2 The Modified Sharpe Ratio 162 5.2.3 The Sortino Ratio 163 5.2.4 The Drawdown Ratio 167 5.3 Common Performance Measures in the Presence of a Market Benchmark 170 5.3.1 The Information Ratio 172 5.3.2 The M-Squared Metric 173 5.3.3 The Treynor Ratio 174 5.3.4 Jensen’s Alpha 178 5.4 The Omega Ratio 181 6 Asset Pricing Models 185 6.1 The Risk-Adjusted Two-Moment Capital Asset Pricing Model 185 6.1.1 Interpreting H 189 6.1.2 Static Alpha Analysis 191 6.1.3 Dynamic Rolling Alpha Analysis 193 6.2 Multi-factor Models 195 6.3 The Choice of Factors 196 6.3.1 A Multi-Factor Framework for a Risk-Adjusted Hedge Fund Alpha League Table 202 6.3.2 Alpha and Beta Separation 208 6.4 Dynamic Style Based Return Analysis 210 6.5 The Markowitz Risk-Adjusted Evaluation Method 214 7 Hedge Fund Market Risk Management 223 7.1 Value-at-Risk 223 7.2 Traditional Measures 226 7.2.1 Historical Simulation 226 7.2.2 Parametric Method 229 7.2.3 Monte Carlo Simulation 230 7.3 Modified VaR 233 7.4 Expected Shortfall 236 7.5 Extreme Value Theory 239 7.5.1 Block Maxima 240 7.5.2 Peaks over Threshold 241 References 245 Important Legal Information 249 Index 251

About the Author :
PAUL DARBYSHIRE gained his PhD in Theoretical Physics from King’s College London and then began his career working has a Quantitative Analyst and Trader at HSBC on the Exotic Derivatives and Structured Products desk. He has subsequently been involved in the development and implementation of a variety of trading and risk management platforms for a number of major investment banks around the globe. Over the past several years Paul has been responsible for the analysis and design of cutting edge algorithms in the development of behavioural finance models at Oxford University. Paul has also provided many private equity firms, hedge funds and asset management companies with consultancy in areas such as dynamic portfolio optimisation, trading platform design, software engineering and risk management. DAVID HAMPTON gained his PhD in Electrical Engineering from the Queen’s University of Belfast and an international MBA from Institut Superieur de Gestion in Paris, New York and Tokyo before joining Bank of America Capital Markets in London. David was previously an Adjunct Finance Professor at Skema Business School in Sophia Antipolis where he taught Financial Engineering and Excel/VBA Programming at the MSc level. At EDHEC Business School in Nice, he was responsible for managing their range of five MSc courses as Assistant Dean of the Financial Economics Track. An NFA registered CTA since 1997, David has been active as a consultant to the hedge fund community and as a Hedge Fund Manager with particular expertise in Global Macro Managed Futures and Long Short Equity investment styles. Both David and Paul are Directors of darbyshirehampton; an innovative quantitative research, advisory, and consultancy firm specialising in hedge funds and the alternative investment industry. Website: www.darbyshirehampton.com.