Use powerful C++ algorithms and Object Oriented Programming (OOP) to aid in hedge fund decision making Low interest rates, overcrowded markets and greater regulatory oversight are just some of the many reasons it is close to impossible for hedge funds to draw competitive returns. The solution for many hedge fund managers, quantitative investment analysts and risk managers is to adopt new technologies, platforms and programming languages to better manage their risks and maximise the benefits of their return profiles.
Hedge Fund Modelling and Analysis is a full course in the latest analytic strategies for hedge fund investing, complete with a one-of-a-kind primer on both C++ and object oriented programming (OOP). Covering both basic and risk-adjusted performance measures, this practitioner's guide enables you to manage risk easily and make the most of key statistics with simple and advanced analysis techniques. This highly anticipated third book in the widely used Hedge Fund Modelling and Analysis series is the only guide available for applying the powerful C++ language to revolutionise hedge fund trading. Even if you've never worked with code before, the focused overview of C++ gives you everything you need to navigate the technical aspects of object oriented programming, which enables you to build sophisticated analysis programs from small units of reusable code. This book is your breakthrough introduction to winning with hedge funds in the new reality of trading.
Jumpstart your new approach to beating the markets with:
- All the guidance and hands-on support you need to use quantitative strategies to optimise hedge fund decision-making.
- Illustrative modelling exercises and worked-out problems demonstrating what to expect when assessing risk and return factors in the real world.
- A companion website offering additional C++ programs, algorithms and data to download.
Make reading Hedge Fund Modelling and Analysis your new routine and gain all the insight and relevant information you need to beat the markets.
Table of Contents:
Preface xi
CHAPTER 1 Essential C++ 1
1.1 A Brief History of C and C++ 1
1.2 A Basic C++ Program 2
1.3 Variables 4
1.3.1 Characters and Strings 5
1.3.2 Variable Declarations 8
1.3.3 Type Casting 9
1.3.4 Variable Scope 10
1.3.5 Constants 11
1.4 Operators 12
1.4.1 The Assignment Operator 12
1.4.2 Arithmetic Operators 14
1.4.3 Relational Operators 15
1.4.4 Logical Operators 16
1.4.5 Conditional Operator 17
1.5 Input and Output 18
1.6 Control Structures 21
1.6.1 Branching 21
1.6.2 Looping 25
1.6.3 The for Loop 25
1.6.4 The while Loop 27
1.6.5 The do…while Loop 29
1.7 Arrays 30
1.8 Vectors 31
1.9 Functions 33
1.9.1 Call-by-Value vs. Call-by-Reference 36
1.9.2 Overloading Functions 39
1.10 Object Oriented Programming 41
1.10.1 Classes and Abstract Data Types 42
1.10.2 Encapsulation and Interfaces 43
1.10.3 Inheritance and Overriding Functions 44
1.10.4 Polymorphism 45
1.10.5 An Example of a Class 46
1.10.6 Getter and Setter Methods 49
1.10.7 Constructors and Destructors 52
1.10.8 A More Detailed Class Example 55
1.10.9 Implementing Inheritance 61
1.10.10 Operator Overloading 64
CHAPTER 2 The Hedge Fund Industry 71
2.1 What are Hedge Funds? 71
2.2 The Structure of a Hedge Fund 74
2.2.1 Fund Administrators 74
2.2.2 Prime Brokers 75
2.2.3 Custodian, Auditors and Legal 76
2.3 The Global Hedge Fund Industry 77
2.3.1 North America 79
2.3.2 Europe 80
2.3.3 Asia 81
2.4 Specialist Investment Techniques 82
2.4.1 Short Selling 82
2.4.2 Leverage 83
2.4.3 Liquidity 84
2.5 Recent Developments for Hedge Funds 85
2.5.1 UCITS Hedge Funds 85
2.5.2 The European Passport 88
2.5.3 Restrictions on Short Selling 88
CHAPTER 3 Hedge Fund Data Sources 91
3.1 Hedge Fund Databases 91
3.2 Major Hedge Fund Indices 92
3.2.1 Non-Investable and Investable Indices 92
3.2.2 Dow Jones Credit Suisse Hedge Fund Indices (www.hedgeindex.com) 94
3.2.3 Hedge Fund Research (www.hedgefundresearch.com) 100
3.2.4 FTSE Hedge (www.ftse.com) 102
3.2.5 Greenwich Alternative Investments (www.greenwichai.com) 104
3.2.6 Morningstar Alternative Investment Center (www. morningstar.com/advisor/alternative-investments.htm) 108
3.2.7 EDHEC Risk and Asset Management Research Centre (www.edhec-risk.com) 112
3.3 Database and Index Biases 113
3.3.1 Survivorship Bias 113
3.3.2 Instant History Bias 115
3.4 Benchmarking 115
3.4.1 Tracking Error 116
CHAPTER 4 Statistical Analysis 119
4.1 The Stats Class 119
4.2 The Utils Class 120
4.3 The Import Class 123
4.4 Basic Performance Plots 127
4.4.1 Value Added Index 127
4.4.2 Histograms 130
4.5 Probability Distributions 131
4.5.1 Populations and Samples 132
4.6 Probability Density Function 133
4.7 Cumulative Distribution Function 134
4.8 The Normal Distribution 134
4.8.1 Standard Normal Distribution 136
4.9 Visual Tests for Normality 136
4.9.1 Inspection 136
4.9.2 Normal Probability Plot 137
4.10 Moments of a Distribution 138
4.10.1 Mean and Standard Deviation 138
4.10.2 Skew 141
4.10.3 Kurtosis 142
4.11 Covariance and Correlation 146
4.12 Linear Regression 158
4.12.1 Coefficient of Determination 163
4.12.2 Residual Plots 167
CHAPTER 5 Performance Measurement 173
5.1 The PMetrics Class 173
5.2 The Intuition Behind Risk-Adjusted Returns 174
5.2.1 Risk-Adjusted Returns 182
5.3 Absolute Risk-Adjusted Return Metrics 184
5.4 The Sharpe Ratio 187
5.5 Market Models 191
5.5.1 The Information Ratio 192
5.5.2 The Treynor Ratio 197
5.5.3 Jensen’s Alpha 203
5.5.4 M-Squared 205
5.6 The Minimum Acceptable Return 207
5.6.1 The Sortino Ratio 207
5.6.2 The Omega Ratio 211
CHAPTER 6 Mean-Variance Optimisation 213
6.1 The Optimise Class 213
6.2 Mean-Variance Analysis 214
6.2.1 Portfolio Return and Variance 214
6.2.2 The Mean-Variance Optimisation Problem 229
6.2.3 The Global Minimum Variance Portfolio 244
6.2.4 Short Sale Constraints 246
CHAPTER 7 Market Risk Management 247
7.1 The RMetrics Class 247
7.2 Value-at-Risk 248
7.3 Traditional VaR Methods 251
7.3.1 Historical Simulation 251
7.3.2 Parametric Method 254
7.3.3 Monte-Carlo Simulation 261
7.4 Modified VaR 263
7.5 Expected Shortfall 266
7.6 Extreme Value Theory 271
7.6.1 Block Maxima 272
7.6.2 Peaks Over Threshold 272
References 277
Index 279
About the Author :
PAUL DARBYSHIRE gained his PhD in Theoretical Physics from King’s College London and then began his career working as 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 investent banks around the globe. Since 2005, Paul has been responsible for the analysis and design of cutting-edge algorithms in the development of behavioural finance and decision-making models at the University of Oxford. Paul also provides many private equity firms, hedge funds and investment management companies with senior 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 1996, 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.
This is the third book in the authorial team’s popular Hedge Fund Modelling and Analysis series, which includes Hedge Fund Modelling and Analysis using MATLAB and Hedge Fund Modelling and Analysis Using Excel and VBA.
