Estimating the correlation between operational risk loss categories over different time horizons

TL;DR

This paper introduces an analytical method to estimate the correlation of operational risk losses across different time horizons, improving accuracy and efficiency over traditional simulation methods.

Contribution

The paper develops a frequency model that analytically computes mean, variance, and co-variances of losses over arbitrary time windows, addressing temporal correlations in operational risk modeling.

Findings

Analytical calculations closely match Monte Carlo simulations.

Loss statistics vary significantly across different time horizons.

Results impact capital requirement estimations for financial institutions.

Abstract

Operational risk is challenging to quantify because of the broad range of categories (fraud, technological issues, natural disasters) and the heavy-tailed nature of realized losses. Operational risk modeling requires quantifying how these broad loss categories are related. We focus on the issue of loss frequencies having different time scales (e.g., daily, yearly, monthly basis), specifically on estimating the statistics of losses on arbitrary time horizons. We present a frequency model where mathematical techniques can be feasibly applied to analytically calculate the mean, variance, and co-variances that are accurate compared to more time-consuming Monte Carlo simulations. We show that the analytic calculations of cumulative loss statistics in an arbitrary time window are feasible here and would otherwise be intractable due to temporal correlations. Our work has potential value because these statistics are crucial for approximating correlations of losses via copulas. We systematically vary all model parameters to demonstrate the accuracy of our methods for calculating all first and second order statistics of aggregate loss distributions. Finally, using combined data from a consortium of institutions, we show that different time horizons can lead to a large range of loss statistics that can significantly affect calculations of capital requirements.