Hedge Fund and Investment Analysis

Day One

Session 1 – Understanding Hedge Fund data  

• Major hedge fund databases
• Database biases:
  • Survivorship
  • Instant History
  • Backfill
• Benchmarking 
• Tracking error
• Hedge fund drivers:
  • Alpha
  • Beta
  • Alternative beta
• Hedge fund indices:
  • Investable
  • Non-investable indices

Session 2 - Statistics for Hedge Funds

• Risk and return measures
• Higher moments:
  • Skewness
  • Kurtosis
• Visual tests of normality:
  • Inspection
  • Normal Q-Q plot
• Linear regression:
  • Ordinary Least Squares (OLS)
  • Coefficient of determination
• Numerical tests of normality:
  • Jarque-Bera test for normality
  • Hypothesis testing
• Portfolio theory:
  • Mean-variance analysis
  • Optimisation

Workshops:

• An empirical hedge fund return distribution
• A Q-Q plot to test for normality
• Performing a regression analysis
• Optimisation a fund of hedge funds allocation

Session 3 - Risk-adjusted performance measures

• Sharpe ratio
• Modified Sharpe ratio
• Sortino ratio
• Drawdown ratio
• Information ratio
• Treynor ratio
• Jensen alpha

Workshop: Analysing a range of risk-adjusted Hedge Fund performance measures 

Day Two

Session 4 - Hedge Funds asset pricing models

• Capital Asset pricing Model (CAPM)
  • Theoretical foundations
  • Validation
  • New extensions and developments
• Multi-factor models:
  • Principal Components Analysis (PCA)
• Style analysis:
  • Radar charts
  • Herfindahl-Hirschmann Index (HHI)
  • Rolling windows
• Kalman filters
• Cluster analysis:
  • Manhattan distance

Workshops:

• Analysing the CAPM market model
• Developing a style analysis model

Session 5 - Market risk management for Hedge Funds

• What is market risk?
• Traditional VaR measures:
  • Historical
  • Covariance
  • Monte-Carlo
• Modified VaR
• Expected shortfall
• Extreme Value Theory:
  • Block maxima
  • Peak over threshold

Workshops: Implementing a range of VaR models for a Hedge Fund