Predicting Operational Loss Exposure Using Past Losses

52 Pages Posted: 5 May 2016 Last revised: 23 Aug 2018

See all articles by Filippo Curti

Filippo Curti

Federal Reserve Banks - Federal Reserve Bank of Richmond

Marco Migueis

Federal Reserve Board

Multiple version iconThere are 2 versions of this paper

Date Written: December 13, 2017

Abstract

Operational risk models, such as the loss distribution approach, frequently use past internal losses to forecast operational loss exposure. However, the ability of past losses to predict exposure, particularly tail exposure, has not been thoroughly examined in the literature. In this paper, we test whether simple metrics derived from past loss experience are predictive of future tail operational loss exposure using quantile regression. We nd evidence that past losses are predictive of future exposure, particularly metrics related to loss frequency.

Keywords: Banking Regulation, Risk Management, Operational Risk, Tail Risk, Quantile Regression

JEL Classification: G21, G28, G32

Suggested Citation

Curti, Filippo and Migueis, Marco, Predicting Operational Loss Exposure Using Past Losses (December 13, 2017). Available at SSRN: https://ssrn.com/abstract=2775427 or http://dx.doi.org/10.2139/ssrn.2775427

Filippo Curti

Federal Reserve Banks - Federal Reserve Bank of Richmond ( email )

P.O. Box 27622
Richmond, VA 23261
United States

Marco Migueis (Contact Author)

Federal Reserve Board ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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