Y. Yamada (Japan) and J.A. Primbs (USA)
Hedging error, Excess kurtosis and skewness, Value-at Risk, Conditional Value-at-Risk
In this paper, we present a Value-at-Risk (VaR) and Con ditional Value-at-Risk (CVaR) estimation technique for dy namic hedging and investigate the effect of higher order moments in the underlying on the hedging loss distribu tions. At first, we approximate the underlying stock process through its first four moments including skewness and kur tosis using a general parameterization of multinomial lat tices, and solve the mean square optimal hedging problem. Then our recently developed technique is applied to extract the hedging loss distributions in option hedge positions. Fi nally, we demonstrate how the hedging error distribution changes with respect to non-zero kurtosis and skewness in the underlying through numerical experiments, and exam ine the relation between VaR and CVaR of the hedging loss distributions and kurtosis of the underlying.
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