Dynamic operational risk: modeling dependence and combining different sources of information

TL;DR

This paper introduces a flexible stochastic model for operational risk dependence that integrates multiple data sources using Bayesian inference and advanced MCMC techniques.

Contribution

It presents a novel stochastic dependence model for operational risks that combines internal, external, and expert data through Bayesian methods.

Findings

Flexible correlation structure for operational risks

Effective Bayesian estimation with Slice sampling

Integration of diverse data sources in risk modeling

Abstract

In this paper, we model dependence between operational risks by allowing risk profiles to evolve stochastically in time and to be dependent. This allows for a flexible correlation structure where the dependence between frequencies of different risk categories and between severities of different risk categories as well as within risk categories can be modeled. The model is estimated using Bayesian inference methodology, allowing for combination of internal data, external data and expert opinion in the estimation procedure. We use a specialized Markov chain Monte Carlo simulation methodology known as Slice sampling to obtain samples from the resulting posterior distribution and estimate the model parameters.