Addressing the impact of data truncation and parameter uncertainty on operational risk estimates

Xiaolin Luo, Pavel V. Shevchenko and John B. Donnelly

Abstract

ABSTRACT

Typically, operational risk losses are reported above some threshold. This paper studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution for operational risk for a broad range of distribution parameters and truncation levels. Loss frequency and severity are modeled with the Poisson and lognormal distributions, respectively. Two cases of ignoring data truncation are studied: the “naive model”, fitting a lognormal distribution with support on a positive semi-infinite interval; and the “shifted model”, fitting a lognormal distribution shifted to the truncation level. For all practical cases, the “naive model” leads to underestimation (that can be severe) of the 0.999 quantile. The “shifted model” overestimates the 0.999 quantile except for some cases of small underestimation for large truncation levels. Conservative estimation of capital charge is usually acceptable and the use of the “shifted model” can be justified while the “naive model” should not be allowed. However, if parameter uncertainty is taken into account (in practice it is often ignored), the “shifted model” can lead to considerable underestimation of capital charge. This is demonstrated with a practical example.