A1106
Title: Longevity risk hedging via non-Gaussian state-space mortality models: A mean-variance-skewness-kurtosis approach
Authors: Johnny S-H Li - The University of Waterloo (Canada)
Wai-Sum Chan - The Hang Seng University of Hong Kong (Hong Kong) [presenting]
Abstract: Longevity risk has recently become a high-profile risk among insurers and pension plan sponsors. One way to mitigate longevity risk is to build a hedge using derivatives that are linked to mortality indexes. Longevity hedging methods are often based on the normality assumption, considering only the variance but no other (higher) moments. However, strong empirical evidence suggests that mortality improvement rates are driven by asymmetric and fat-tailed distributions, so that existing longevity hedging methods should be expanded to incorporate higher moments. The purpose is to fill the gap by adopting a mean-variance-skewness-kurtosis approach based on non-Gaussian extensions of commonly used stochastic mortality models, formulated in a state-space setting. On the basis of a general representation of these models, the authors derive (approximate) analytical expressions for the moments of the present values of the hedging instruments and the liability being hedged. These expressions are then integrated with a polynomial goal programming model, from which the optimal hedge portfolio is identified. Finally, theoretical results are demonstrated with a real mortality data set and a range of hedger preferences.
