Abstract
It has been frequently discussed, that returns are not normally distributed. Liquidity costs, measuring market liquidity, are similarly non-normally distributed displaying fat tails and skewness. Liquidity risk models either ignore this fact or use the historical distribution to empirically estimate worst losses. We suggest a new and easily implementable, parametric approach based on the Cornish-Fisher approximation to account for non-normality in liquidity risk. We show how to implement this methodology in a large sample of stocks and provide evidence that it produces much more accurate results than an alternative empirical risk estimation.
Published Version
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