Estimation and Testing of Portfolio Value-at-Risk Based on L-Comoment Matrices

摘要

This study employs L-comoments introduced by Serfling and Xiao (2007) into portfolio Value-at-Risk estimation through two models: the Cornish-Fisher expansion (Draper, N. R. & Tierney, D. E., 1973) and modified VaR (Zangari, P., 1996). Backtesting outcomes indicate that modified VaR outperforms and L-comoments give better estimates of portfolio skewness and excess kurtosis than do classical central moments in modeling heavy-tailed distributions.