Journal of Operational Risk
ISSN:
1744-6740 (print)
1755-2710 (online)
Editor-in-chief: Marcelo Cruz
![Risk.net](https://www.risk.net/sites/default/files/styles/print_logo/public/2018-09/print-logo.png?itok=1TpHrpuP)
Optimal B-robust posterior distributions for operational risk
Ivan Luciano Danesi, Fabio Piacenza, Erlis Ruli and Laura Ventura
Need to know
- Computationally efficient method for robust Bayesian estimation of AMA-LDA models
- Bayesian capital estimates insensitive to extremely high or extremely low losses
- Bayesian estimates which are more stable than maximum likelihood-based counterparts
- Bayesian estimates which are reasonably efficient
Abstract
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 OBRestimating 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 proposedABC-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.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net