Modeling the yearly Value-at-Risk for operational risk in Chinese commercial banks
In this paper, we explore the loss data collection exercise for operational risk in Chinese commercial banks from 1999 to first half of 2006. Firstly, the above data are bootstrapped to analyze the capital allocation for a medium-scaled commercial bank in China. Secondly, for every selected cell, we calibrate two truncated distributions to fit the loss severity, one for 'normal' losses and the other for the 'extreme' losses. Moreover, a more realistic dependence structure - multivariate t copula function is used to measure the relation among the selected cells. In the final, the simulation results suggest that substantial savings can be achieved through measuring the dependence by means of multivariate t copula function than by means of perfect positive dependence. (C) 2011 IMACS. Published by Elsevier B.V. All rights reserved.
value-at-risk operational risk multivariate t copula loss distribution approach monte carlo mixture distribution