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Hedge Fund Risks Simulation
 

Hedge Fund Risks Simulation

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Presentation for University of Connecticut MBA Program in "Absolute Return Strategies"

Presentation for University of Connecticut MBA Program in "Absolute Return Strategies"

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Hedge Fund Risks Simulation Hedge Fund Risks Simulation Presentation Transcript

  • Portfolio Simulation using R and RMetrics Kevin Ashley, Phuong Le, Kara Horvath, Doreen Gasaatura University of Connecticut, MBA
  • Our approach
      • Calculate risk / return for the current portfolio
      • Bucket assets into traditional, alternative liquid and illiquid classes
      • Define constraints: Long-Only, CVaR
      • Solve the minimization problem: Minimize Risk. What method? MV or CVaR (skewed - quadratic solver).
      • Optimize for “Minimum Risk” or “Maximum Risk”
      • Measure and explain risk metrics
    Long Only Min CVaR Optimization Problem
  • Risk Dashboard Our Risk Dashboard summarizes all risk statistics we calculated. We'll be returning to this slide frequently.
  • Risk Optimization with R: Some Capabilities
      • Diversification (Markowitz, 1952)
      • Covariance Risk Budgeting (finite resource)
      • Tail Risk Budgeting (Copulae, dependence)
      • CVaR optimization (Rockafeller, Uryasev, 1992)
  • Current Portfolio We compare the Current portfolio, with given weights to the simulated portfolios for Growth and Inflationary environments
  • Low Risk Portfolio
      • Min. Risk portfolio is reasonably diversified with higher concentration in TIPS, Investment Grade Bond and some alternative strategies (particularly fixed income). The Calmar shows that this may a more sensible risk-adjusted portfolio than Max.Return or Current.
  • Return Maximization Portfolio
      • The return maximization portfolio consists of Emerging Markets Equities, Midcap Equities, Global Macro and others. The diversification is less than Risk Minimization portfolio. This allocation maximizes returns but has a significant risk.
  • Efficient Frontier: Other Possibilities
  • Rolling Performance Summary: “Current”,”Inflation” and “Growth” Portfolios ROR Std Sharpe
  • Omega Plots MAR - minimal acceptable return, 0.1/12 monthly [a,b] interval for which the distribution of the asset return is defined
  • VaR Sensitivity How different VaR measurements are sensitive to changes in confidence intervals. We can see that the low-risk portfolio is least sensitive to changes in confidence levels.
  • Rolling Correlation to S&P 500
      • One of the benefits of our “LowRisk” and “MaxReturn” portfolios is lower correlation to S&P 500, compared to the Current portfolio. The current portfolio has a correlation to S&P which is very close to 1.
  • Drawdowns for “Current”,”Inflation” and “Growth” Portfolios 1 2 -0.020218 11/30/2005 10/31/2005 2 3 -0.028018 6/30/2004 4/30/2004 4 5 -0.028689 9/30/2006 5/31/2006 2 4 -0.033581 6/30/2005 3/31/2005 1 11 -0.119455 NA 11/30/2007 Recovery Length Depth To From 1 2 -0.002254 4/30/2005 3/31/2005 1 3 -0.005812 6/30/2004 4/30/2004 2 3 -0.011916 9/30/2003 7/31/2003 Recovery Length Depth To From 1 2 -0.025694 11/30/2005 10/31/2005 5 6 -0.030773 10/31/2006 5/31/2006 2 4 -0.038126 6/30/2005 3/31/2005 1 4 -0.039592 NA 6/30/2008 4 7 -0.068138 5/31/2008 11/30/2007 Recovery Length Depth To From In terms of the Drawdown, we’re much better off with both simulated portfolios, the Min Risk being very low drawdown.
  • Conclusion
      • We solved optimization problem both ways: to create a "Maximum Return" and "Minimum Risk" given the weight constraints and CVaR downside risk measure.
      • Our portfolios are significantly better constructed than the initial one (drawdown, downside risk, ratios).