Hedge Fund Modelling and Analysis Using Excel and VBA

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.

• Preface xi1 The Hedge Fund Industry 11.1 What Are Hedge Funds? 11.2 The Structure of a Hedge Fund 41.2.1 Fund Administrators 51.2.2 Prime Brokers 51.2.3 Custodian, Auditors and Legal 61.3 The Global Hedge Fund Industry 71.3.1 North America 81.3.2 Europe 101.3.3 Asia 111.4 Specialist Investment Techniques 121.4.1 Short Selling 121.4.2 Leverage 141.4.3 Liquidity 151.5 New Developments for Hedge Funds 161.5.1 UCITS III Hedge Funds 161.5.2 The European Passport 191.5.3 Restrictions on Short Selling 202 Major Hedge Fund Strategies 232.1 Single- and Multi-Strategy Hedge Funds 232.2 Fund of Hedge Funds 252.3 Hedge Fund Strategies 272.3.1 Tactical Strategies 282.3.1.1 Global Macro 282.3.1.2 Managed Futures 312.3.1.3 Long/Short Equity 362.3.1.4 Pairs Trading 382.3.2 Event-Driven 422.3.2.1 Distressed Securities 422.3.2.2 Merger Arbitrage 462.3.3 Relative Value 492.3.3.1 Equity Market Neutral 492.3.3.2 Convertible Arbitrage 502.3.3.3 Fixed Income Arbitrage 542.3.3.3.1 Capital Structure Arbitrage 562.3.3.3.2 Swap-Spread Arbitrage 572.3.3.3.3 Yield Curve Arbitrage 583 Hedge Fund Data Sources 613.1 Hedge Fund Databases 613.2 Major Hedge Fund Indices 653.2.1 Non-investable and Investable Indices 663.2.2 Dow Jones Credit Suisse Hedge Fund Indexes 683.2.2.1 Liquid Alternative Betas 703.2.3 Hedge Fund Research 733.2.4 HedgeFund.net 773.2.5 FTSE Hedge 773.2.5.1 FTSE Hedge Momentum Index 783.2.6 Greenwich Alternative Investments 793.2.6.1 GAI Investable Indices 803.2.7 Morningstar Alternative Investment Center 833.2.7.1 MSCI Hedge Fund Classification Standard 833.2.7.2 MSCI Investable Indices 853.2.8 EDHEC Risk and Asset Management Research Centre (www.edhec-risk.com) 863.3 Database and Index Biases 883.3.1 Survivorship Bias 893.3.2 Instant History Bias 903.4 Benchmarking 913.4.1 Tracking Error 92Appendix A: Weighting Schemes 954 Statistical Analysis 994.1 Basic Performance Plots 994.1.1 Value Added Monthly Index 994.1.2 Histograms 1024.2 Probability Distributions 1054.2.1 Populations and Samples 1064.3 Probability Density Function 1074.4 Cumulative Distribution Function 1084.5 The Normal Distribution 1094.5.1 Standard Normal Distribution 1104.6 Visual Tests for Normality 1114.6.1 Inspection 1114.6.2 Normal Q-Q Plot 1124.7 Moments of a Distribution 1144.7.1 Mean and Standard Deviation 1144.7.2 Skewness 1174.7.3 Excess Kurtosis 1194.7.4 Data Analysis Tool: Descriptive Statistics 1204.8 Geometric Brownian Motion 1224.8.1 Uniform Random Numbers 1254.9 Covariance and Correlation 1264.10 Regression Analysis 1314.10.1 Ordinary Least Squares 1314.10.1.1 Coefficient of Determination 1334.10.1.2 Residual Plots 1344.10.1.3 Jarque–Bera Normality Test 1354.10.1.4 Data Analysis Tool: Regression 1384.11 Portfolio Theory 1424.11.1 Mean–Variance Analysis 1424.11.2 Solver: Portfolio Optimisation 1454.11.3 Efficient Portfolios 1485 Risk-Adjusted Return Metrics 1515.1 The Intuition behind Risk-Adjusted Returns 1525.1.1 Risk-Adjusted Returns 1545.2 Common Risk-Adjusted Performance Ratios 1575.2.1 The Sharpe Ratio 1605.2.2 The Modified Sharpe Ratio 1625.2.3 The Sortino Ratio 1635.2.4 The Drawdown Ratio 1675.3 Common Performance Measures in the Presence of a Market Benchmark 1705.3.1 The Information Ratio 1725.3.2 The M-Squared Metric 1735.3.3 The Treynor Ratio 1745.3.4 Jensen’s Alpha 1785.4 The Omega Ratio 1816 Asset Pricing Models 1856.1 The Risk-Adjusted Two-Moment Capital Asset Pricing Model 1856.1.1 Interpreting H 1896.1.2 Static Alpha Analysis 1916.1.3 Dynamic Rolling Alpha Analysis 1936.2 Multi-factor Models 1956.3 The Choice of Factors 1966.3.1 A Multi-Factor Framework for a Risk-Adjusted Hedge Fund Alpha League Table 2026.3.2 Alpha and Beta Separation 2086.4 Dynamic Style Based Return Analysis 2106.5 The Markowitz Risk-Adjusted Evaluation Method 2147 Hedge Fund Market Risk Management 2237.1 Value-at-Risk 2237.2 Traditional Measures 2267.2.1 Historical Simulation 2267.2.2 Parametric Method 2297.2.3 Monte Carlo Simulation 2307.3 Modified VaR 2337.4 Expected Shortfall 2367.5 Extreme Value Theory 2397.5.1 Block Maxima 2407.5.2 Peaks over Threshold 241References 245Important Legal Information 249Index 251