(Discussant: Hua Chen)
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  • It is a well organized and nicely written paper. Most important of all, it makes significant contributions to the existing literature

(Discussant: Hua Chen) (Discussant: Hua Chen) Presentation Transcript

  • Discussion on “Hedging Longevity Risk by Asset Management – an ALM Approach” Hua Chen Temple University September 26, 2009
  • Framework of This Paper Mortality Risk (CBD model) Longevity Bond Optimal Hedging? (Mean-Variance) Interest Rate Risk (CIR model) Risk-free Bond Coupon Bond Annuity Providers
  • Main Contributions
    • Consider mortality risk and interest rate risk simultaneously
    • Employ Mean-Variance Analysis for asset allocation
    • The usage of longevity bond can significantly reduce the aggregate risk
  • Structured ALM Models
    • Static Models
      • Hedge against small changes from the current state of the world.
      • A term structure is input to the model which matches assets and liabilities under this structure.
      • Conditions are then imposed to guarantee that if the term structure deviates somewhat from the assumed value, the assets and liabilities will move in the same direction and by equal amounts.
      • Portfolio immunization
      • Does not permit the specification of a stochastic process that describes changes of the economic conditions
  • Structured ALM Models
    • Single Period, stochastic model
      • A stochastic ALM model
        • Describes the distribution of returns of both assets and liabilities
        • Ensures movements of both sides are highly correlated.
      • One period - Myopic
        • It does not account for the necessity to rebalance the portfolio once some surplus is realized.
        • It does not recognize the fact that different portfolio may be appropriate to capture the correlations
  • Structured ALM Models
    • Multiperiod, dynamic and stochastic Model
      • Captures both the stochastic nature of the problem, but also the fact that the portfolio is managed in a dynamic,multiperiod context.
      • Dynamic Programming
  • Different Risk Measures
    • Other dispersion measures
    • e.g., Mean-Absolute Deviation (MAD)
      • More robust estimator of scale
      • Behaves better with distributions without a mean or variance, such as the Cauchy distribution.
  • Different Risk Measures
    • Higher moment
    • e.g., Mean-Variance-Skewness (MVS) (Boyle and Ding, 2006)
    • Mitton and Vorkink (2007)
    • Apparent MV inefficiency of underdiversified investors can be largely explained by the fact that investors sacrifice MV efficiency for higher skewness exposure.
    • Because a higher skewness means greater likelihood of a large return.
  • Different Risk Measures
    • Tail measures
    • e.g., VaR
      • does not consider the magnitude of loss
      • undesirable properties such as lack of sub-additivity,
    • e.g., CVaR
      • Unlike MV and MAD penalizing both the desirable upside and the undesirable downside outcomes
      • Unlike VaR, CVaR not only consider probability but also size of loss.
      • More consistent risk measure than VaR since it is sub-additive and convex.
      • Unlike MVS, it can be optimized using linear programming (LP) and nonsmooth optimization algorithms, computational efficiency.
  • Questions?
  • Question? How to estimate?