Optimal B-Robust Posterior Distributions for Operational Risk

20 Pages Posted: 14 Nov 2016

See all articles by Ivan Luciano Danesi

Ivan Luciano Danesi

UniCredit Business Integrated Solutions S.C.p.A.

Fabio Piacenza

UniCredit S.p.A.

Erlis Ruli

University of Padua - Department of Statistical Sciences

Laura Ventura

University of Padua - Department of Statistical Sciences

Date Written: November 14, 2016

Abstract

The aim of operational risk modeling is to provide a reasonably accurate, reasonably precise and reasonably robust estimation of capital requirements, including a level of sensitivity that is consistent with the changes of the risk profile. A way to obtain robust capital estimates is through optimal B-robust (OBR) methods. Previous research has shown that OBR methods might mitigate the bias in capital risk quantification when compared with classical maximum likelihood estimation. Motivated by requirements related to operational risk measurement, the aim of this work is to integrate prior information into a robust parameter estimation framework via OBR-estimating functions. Unfortunately, the evaluation of OBR-estimating functions for different parameter values is cumbersome, and this rules out the use of many pseudo-likelihood methods. To deal with this issue, we suggest resorting to approximate Bayesian computation (ABC) machinery, using the OBR-estimating function as the summary statistic. Unlike other methods, the proposed ABC-OBR algorithm requires the evaluation of the OBR-estimating function at a fixed parameter value but using different data samples, which is computationally trivial. The method is illustrated using a small simulation study and applications to two real operational risk data sets.

Keywords: advanced measurement approach (AMA), approximate Bayesian computation (ABC), loss distribution approach, operational risk, robust posterior distribution, unbiased estimating function

Suggested Citation

Danesi, Ivan Luciano and Piacenza, Fabio and Ruli, Erlis and Ventura, Laura, Optimal B-Robust Posterior Distributions for Operational Risk (November 14, 2016). Journal of Operational Risk, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2868966

Ivan Luciano Danesi

UniCredit Business Integrated Solutions S.C.p.A. ( email )

via Livio Cambi 1
Milan, 20151
Italy

Fabio Piacenza

UniCredit S.p.A. ( email )

Piazza Gae Aulenti 3
Milan, 20154
Italy

Erlis Ruli (Contact Author)

University of Padua - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

Laura Ventura

University of Padua - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
0
Abstract Views
235
PlumX Metrics