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Hedge Funds: Quantitative Insights




Hedge Funds: Quantitative Insights

François-Serge Lhabitant

ISBN: 978-0-470-68777-2 August 2009 354 Pages

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"An excellent and comprehensive source of information on hedge funds! From a quantitative view Lhabitant has done it once again by meticulously looking at the important topics in the hedge fund industry. This book has a tremendous wealth of information and is a valuable addition to the hedge fund literature. In addition, it will benefit institutional investors, high net worth individuals, academics and anyone interested in learning more about this fascinating and often mysterious world of privately managed money. Written by one of the most respected practitioners and academics in the area of hedge funds." -Greg N. Gregoriou, Professor of finance and research coordinator in the School of Business and Economics at Plattsburgh State University of New York.

"This is a landmark book on quantitative approaches to hedge funds. All those with a stake in building hedge fund portfolios will highly profit from this exhaustive guide. A must read for all those involved in hedge fund investing." -Pascal Botteron, Ph.D., Head of Hedge Fund Product Development, Pictet Asset Management

"François-Serge Lhabitant's second book will prove to be a bestseller too - just like Hedge Funds: Myths and Limits. He actually manages to make quantitative analysis 'approachable'- even for those less gifted with numbers. This book, like its predecessor, includes an unprecedented mix of common sense and sophisticated technique. A fantastic guide to the 'nuts and bolts' of hedge fund analysis and a 'must' for every serious investor." -Barbara Rupf Bee, Head of Alternative Fund Investment Group, HSBC Private Bank, Switzerland

"An excellent book, providing deep insights into the complex quantitative analysis of hedge funds in the most lucid and intuitive manner. A must-have supplement to Lhabitant's first book dealing with the mystical and fascinating world of hedge funds. Highly recommended!" -Vikas Agarwal, Assistant Professor of Finance, J. Mack Robinson College of Business, Georgia State University

"Lhabitant has done it again! Whereas most books on hedge funds are nothing more than glorified marketing brochures, Lhabitant's new book tells it how it is in reality. Accessible and understandable but at the same time thorough and critical." -Harry M. Kat, Ph.D., Professor of Risk Management and Director Alternative Investment Research Centre, Cass Business School, City University

"Lhabitant's latest work on hedge funds yet again delivers on some ambitious promises. Successfully bridging theory and practice in a highly accessible manner, those searching for a thorough yet unintimidating introduction to the quantitative methods of hedge fund analysis will not be disappointed." -Christopher L. Culp, Ph.D., Adjunct Professor of Finance, Graduate School of Business, The University of Chicago and Principal, Chicago Partners LLC

Foreword by Mark Anson.




1 Characteristics of Hedge Funds.

1.1 What are hedge funds?

1.2 Investment styles.

1.2.1 The tactical trading investment style.

1.2.2 The equity long/short style.

1.2.3 The event-driven style.

1.2.4 The relative value arbitrage style.

1.2.5 Funds of funds and multi-strategy funds.

1.3 The current state of the hedge fund industry.

2 Measuring Return.

2.1 The difficulties of obtaining information.

2.2 Equalization, crystallization and multiple share classes.

2.2.1 The inequitable allocation of incentive fees.

2.2.2 The free ride syndrome.

2.2.3 Onshore versus offshore funds.

2.2.4 The multiple share approach.

2.2.5 The equalization factor/depreciation deposit approach.

2.2.6 Simple equalization.

2.2.7 Consequences for performance calculation.

2.3 Measuring returns.

2.3.1 The holding period return.

2.3.2 Annualizing.

2.3.3 Multiple hedge fund aggregation.

2.3.4 Continuous compounding.

3 Return and Risk Statistics.

3.1 Calculating return statistics.

3.1.1 Central tendency statistics.

3.1.2 Gains versus losses.

3.2 Measuring risk.

3.2.1 What is risk?

3.2.2 Range, quartiles and percentiles.

3.2.3 Variance and volatility (standard deviation).

3.2.4 Some technical remarks on measuring historical volatility/variance.

3.2.5 Back to histograms, return distributions and z-scores.

3.3 Downside risk measures.

3.3.1 From volatility to downside risk.

3.3.2 Semi-variance and semi-deviation.

3.3.3 The shortfall risk measures.

3.3.4 Value at risk.

3.3.5 Drawdown statistics.

3.4 Benchmark-related statistics.

3.4.1 Intuitive benchmark-related statistics.

3.4.2 Beta and market risk.

3.4.3 Tracking error.

4 Risk-Adjusted Performance Measures.

4.1 The Sharpe ratio.

4.1.1 Definition and interpretation.

4.1.2 The Sharpe ratio as a long/short position.

4.1.3 The statistics of Sharpe ratios.

4.2 The Treynor ratio and Jensen alpha.

4.2.1 The CAPM.

4.2.2 The market model.

4.2.3 The Jensen alpha.

4.2.4 The Treynor ratio.

4.2.5 Statistical significance.

4.2.6 Comparing Sharpe, Treynor and Jensen.

4.2.7 Generalizing the Jensen alpha and the Treynor ratio.

4.3 M2, M3 and Graham–Harvey.

4.3.1 The M2 performance measure.

4.3.2 GH1 and GH2.

4.4 Performance measures based on downside risk.

4.4.1 The Sortino ratio.

4.4.2 The upside potential ratio.

4.4.3 The Sterling and Burke ratios.

4.4.4 Return on VaR (RoVaR).

4.5 Conclusions.

5 Databases, Indices and Benchmarks.

5.1 Hedge fund databases.

5.2 The various biases in hedge fund databases.

5.2.1 Self-selection bias.

5.2.2 Database/sample selection bias.

5.2.3 Survivorship bias.

5.2.4 Backfill or instant history bias.

5.2.5 Infrequent pricing and illiquidity bias.

5.3 From databases to indices.

5.3.1 Index construction.

5.3.2 The various indices available and their differences.

5.3.3 Different indices–different returns.

5.3.4 Towards pure hedge fund indices.

5.4 From indices to benchmarks.

5.4.1 Absolute benchmarks and peer groups.

5.4.2 The need for true benchmarks.


6 Covariance and Correlation.

6.1 Scatter plots.

6.2 Covariance and correlation.

6.2.1 Definitions.

6.2.2 Another interpretation of correlation.

6.2.3 The Spearman rank correlation.

6.3 The geometry of correlation and diversification.

6.4 Why correlation may lead to wrong conclusions.

6.4.1 Correlation does not mean causation.

6.4.2 Correlation only measures linear relationships.

6.4.3 Correlations may be spurious.

6.4.4 Correlation is not resistant to outliers.

6.4.5 Correlation is limited to two variables.

6.5 The question of statistical significance.

6.5.1 Sample versus population.

6.5.2 Building the confidence interval for a correlation.

6.5.3 Correlation differences.

6.5.4 Correlation when heteroscedasticity is present.

7 Regression Analysis.

7.1 Simple linear regression.

7.1.1 Reality versus estimation.

7.1.2 The regression line in a perfect world.

7.1.3 Estimating the regression line.

7.1.4 Illustration of regression analysis: Andor Technology.

7.1.5 Measuring the quality of a regression: multiple R, R2, ANOVA and p-values.

7.1.6 Testing the regression coefficients.

7.1.7 Reconsidering Andor Technology.

7.1.8 Simple linear regression as a predictive model.

7.2 Multiple linear regression.

7.2.1 Multiple regression.

7.2.2 Illustration: analyzing the Grossman Currency Fund.

7.3 The dangers of model specification.

7.3.1 The omitted variable bias.

7.3.2 Extraneous variables.

7.3.3 Multi-collinearity.

7.3.4 Heteroscedasticity.

7.3.5 Serial correlation.

7.4 Alternative regression approaches.

7.4.1 Non-linear regression.

7.4.2 Transformations.

7.4.3 Stepwise regression and automatic selection procedures.

7.4.4 Non-parametric regression.

8 Asset Pricing Models.

8.1 Why do we need a factor model?

8.1.1 The dimension reduction.

8.2 Linear single-factor models.

8.2.1 Single-factor asset pricing models.

8.2.2 Example: the CAPM and the market model.

8.2.3 Application: the market model and hedge funds.

8.3 Linear multi-factor models.

8.3.1 Multi-factor models.

8.3.2 Principal component analysis.

8.3.3 Common factor analysis.

8.3.4 How useful are multi-factor models?

8.4 Accounting for non-linearity.

8.4.1 Introducing higher moments: co-skewness and co-kurtosis.

8.4.2 Conditional approaches.

8.5 Hedge funds as option portfolios.

8.5.1 The early theoretical models.

8.5.2 Modeling hedge funds as option portfolios.

8.6 Do hedge funds really produce alpha?

9 Styles, Clusters and Classification.

9.1 Defining investment styles.

9.2 Style analysis.

9.2.1 Fundamental style analysis.

9.2.2 Return-based style analysis.

9.2.3 The original model.

9.2.4 Application to hedge funds.

9.2.5 Rolling window analysis.

9.2.6 Statistical significance.

9.2.7 The dangers of misusing style analysis.

9.3 The Kalman filter.

9.4 Cluster analysis.

9.4.1 Understanding cluster analysis.

9.4.2 Clustering methods.

9.4.3 Applications of clustering techniques.


10 Revisiting the Benefits and Risks of Hedge Fund Investing.

10.1 The benefits of hedge funds.

10.1.1 Superior historical risk/reward trade-off.

10.1.2 Low correlation to traditional assets.

10.1.3 Negative versus positive market environments.

10.2 The benefits of individual hedge fund strategies.

10.3 Caveats of hedge fund investing.

11 Strategic Asset Allocation – From Portfolio Optimizing to Risk Budgeting.

11.1 Strategic asset allocation without hedge funds.

11.1.1 Identifying the investor’s financial profile: the concept of utility functions.

11.1.2 Establishing the strategic asset allocation.

11.2 Introducing hedge funds in the asset allocation.

11.2.1 Hedge funds as a separate asset class.

11.2.2 Hedge funds versus traditional asset classes.

11.2.3 Hedge funds as traditional asset class substitutes.

11.3 How much to allocate to hedge funds?

11.3.1 An informal approach.

11.3.2 The optimizers’ answer: 100% in hedge funds.

11.3.3 How exact is mean–variance?

11.3.4 Static versus dynamic allocations.

11.3.5 Dealing with valuation biases and autocorrelation.

11.3.6 Optimizer’s inputs and the GIGO syndrome.

11.3.7 Non-standard efficient frontiers.

11.3.8 How much to allocate to hedge funds?

11.4 Hedge funds as portable alpha overlays.

11.5 Hedge funds as sources of alternative risk exposure.

12 Risk Measurement and Management.

12.1 Value at risk.

12.1.1 Value at risk (VaR) is the answer.

12.1.2 Traditional VaR approaches.

12.1.3 The modified VaR approach.

12.1.4 Extreme values.

12.1.5 Approaches based on style analysis.

12.1.6 Extension for liquidity: L-VaR.

12.1.7 The limits of VaR and stress testing.

12.2 Monte Carlo simulation.

12.2.1 Monte Carlo for hedge funds.

12.2.2 Looking in the tails.

12.3 From measuring to managing risk.

12.3.1 The benefits of diversification.

13 Conclusions.

Online References.