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| Foreword | |
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| Introduction | |
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| Acknowledgments | |
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| Measuring Return and Risk | |
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| Characteristics of Hedge Funds | |
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| What are hedge funds? | |
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| Investment styles | |
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| The tactical trading investment style | |
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| The equity long/short style | |
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| The event-driven style | |
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| The relative value arbitrage style | |
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| Funds of funds and multi-strategy funds | |
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| The current state of the hedge fund industry | |
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| Measuring Return | |
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| The difficulties of obtaining information | |
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| Equalization, crystallization and multiple share classes | |
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| The inequitable allocation of incentive fees | |
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| The free ride syndrome | |
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| Onshore versus offshore funds | |
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| The multiple share approach | |
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| The equalization factor/depreciation deposit approach | |
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| Simple equalization | |
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| Consequences for performance calculation | |
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| Measuring returns | |
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| The holding period return | |
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| Annualizing | |
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| Multiple hedge fund aggregation | |
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| Continuous compounding | |
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| Return and Risk Statistics | |
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| Calculating return statistics | |
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| Central tendency statistics | |
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| Gains versus losses | |
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| Measuring risk | |
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| What is risk? | |
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| Range, quartiles and percentiles | |
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| Variance and volatility (standard deviation) | |
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| Some technical remarks on measuring historical volatility/variance | |
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| Back to histograms, return distributions and z-scores | |
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| Downside risk measures | |
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| From volatility to downside risk | |
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| Semi-variance and semi-deviation | |
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| The shortfall risk measures | |
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| Value at risk | |
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| Drawdown statistics | |
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| Benchmark-related statistics | |
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| Intuitive benchmark-related statistics | |
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| Beta and market risk | |
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| Tracking error | |
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| Risk-Adjusted Performance Measures | |
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| The Sharpe ratio | |
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| Definition and interpretation | |
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| The Sharpe ratio as a long/short position | |
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| The statistics of Sharpe ratios | |
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| The Treynor ratio and Jensen alpha | |
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| The CAPM | |
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| The market model | |
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| The Jensen alpha | |
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| The Treynor ratio | |
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| Statistical significance | |
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| Comparing Sharpe, Treynor and Jensen | |
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| Generalizing the Jensen alpha and the Treynor ratio | |
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| M[superscript 2], M[superscript 3] and Graham-Harvey | |
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| The M[superscript 2] performance measure | |
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| GH1 and GH2 | |
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| Performance measures based on downside risk | |
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| The Sortino ratio | |
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| The upside potential ratio | |
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| The Sterling and Burke ratios | |
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| Return on VaR (Ro VaR) | |
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| Conclusions | |
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| Databases, Indices and Benchmarks | |
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| Hedge fund databases | |
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| The various biases in hedge fund databases | |
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| Self-selection bias | |
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| Database/sample selection bias | |
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| Survivorship bias | |
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| Backfill or instant history bias | |
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| Infrequent pricing and illiquidity bias | |
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| From databases to indices | |
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| Index construction | |
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| The various indices available and their differences | |
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| Different indices-different returns | |
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| Towards pure hedge fund indices | |
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| From indices to benchmarks | |
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| Absolute benchmarks and peer groups | |
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| The need for true benchmarks | |
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| Understanding the Nature of Hedge Fund Returns and Risks | |
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| Covariance and Correlation | |
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| Scatter plots | |
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| Covariance and correlation | |
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| Definitions | |
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| Another interpretation of correlation | |
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| The Spearman rank correlation | |
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| The geometry of correlation and diversification | |
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| Why correlation may lead to wrong conclusions | |
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| Correlation does not mean causation | |
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| Correlation only measures linear relationships | |
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| Correlations may be spurious | |
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| Correlation is not resistant to outliers | |
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| Correation is limited to two variables | |
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| The question of statistical significance | |
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| Sample versus population | |
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| Building the confidence interval for a correlation | |
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| Correlation differences | |
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| Correlation when heteroscedasticity is present | |
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| Regression Analysis | |
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| Simple linear regression | |
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| Reality versus estimation | |
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| The regression line in a perfect world | |
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| Estimating the regression line | |
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| Illustration of regression analysis: Andor Technology | |
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| Measuring the quality of a regression: multiple R, R[superscript 2], ANOVA and p-values | |
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| Testing the regression coefficients | |
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| Reconsidering Andor Technology | |
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| Simple linear regression as a predictive model | |
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| Multiple linear regression | |
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| Multiple regression | |
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| Illustration: analyzing the Grossman Currency Fund | |
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| The dangers of model specification | |
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| The omitted variable bias | |
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| Extraneous variables | |
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| Multi-collinearity | |
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| Heteroscedasticity | |
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| Serial correlation | |
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| Alternative regression approaches | |
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| Non-linear regression | |
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| Transformations | |
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| Stepwise regression and automatic selection procedures | |
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| Non-parametric regression | |
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| Asset Pricing Models | |
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| Why do we need a factor model? | |
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| The dimension reduction | |
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| Linear single-factor models | |
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| Single-factor asset pricing models | |
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| Example: the CAPM and the market model | |
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| Application: the market model and hedge funds | |
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| Linear multi-factor models | |
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| Multi-factor models | |
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| Principal component analysis | |
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| Common factor analysis | |
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| How useful are multi-factor models? | |
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| Accounting for non-linearity | |
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| Introducing higher moments: co-skewness and co-kurtosis | |
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| Conditional approaches | |
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| Hedge funds as option portfolios | |
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| The early theoretical models | |
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| Modeling hedge funds as option portfolios | |
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| Do hedge funds really produce alpha? | |
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| Styles, Clusters and Classification | |
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| Defining investment styles | |
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| Style analysis | |
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| Fundamental style analysis | |
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| Return-based style analysis | |
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| The original model | |
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| Application to hedge funds | |
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| Rolling window analysis | |
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| Statistical significance | |
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| The dangers of misusing style analysis | |
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| The Kalman filter | |
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| Cluster analysis | |
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| Understanding cluster analysis | |
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| Clustering methods | |
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| Applications of clustering techniques | |
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| Allocating Capital to Hedge Funds | |
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| Revisiting the Benefits and Risks of Hedge Fund Investing | |
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| The benefits of hedge funds | |
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| Superior historical risk/reward trade-off | |
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| Low correlation to traditional assets | |
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| Negative versus positive market environments | |
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| The benefits of individual hedge fund strategies | |
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| Caveats of hedge fund investing | |
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| Strategic Asset Allocation--From Portfolio Optimizing to Risk Budgeting | |
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| Strategic asset allocation without hedge funds | |
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| Identifying the investor's financial profile: the concept of utility functions | |
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| Establishing the strategic asset allocation | |
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| Introducing hedge funds in the asset allocation | |
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| Hedge funds as a separate asset class | |
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| Hedge funds versus traditional asset classes | |
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| Hedge funds as traditional asset class substitutes | |
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| How much to allocate to hedge funds? | |
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| An informal approach | |
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| The optimizers' answer: 100% in hedge funds | |
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| How exact is mean-variance? | |
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| Static versus dynamic allocations | |
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| Dealing with valuation biases and autocorrelation | |
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| Optimizer's inputs and the GIGO syndrome | |
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| Non-standard efficient frontiers | |
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| How much to allocate to hedge funds? | |
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| Hedge funds as portable alpha overlays | |
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| Hedge funds as sources of alternative risk exposure | |
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| Risk Measurement and Management | |
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| Value at risk | |
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| Value at risk (VaR) is the answer | |
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| Traditional VaR approaches | |
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| The modified VaR approach | |
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| Extreme values | |
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| Approaches based on style analysis | |
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| Extension for liquidity: L-VaR | |
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| The limits of VaR and stress testing | |
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| Monte Carlo simulation | |
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| Monte Carlo for hedge funds | |
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| Looking in the tails | |
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| From measuring to managing risk | |
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| The benefits of diversification | |
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| Conclusions | |
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| Online References | |
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| Bibliography | |
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| Index | |