A Bayesian Networks Approach to Operational Risk

Physica A, Vol. 389, No. 8, pp. 1721-1728, 2010

Posted: 10 Sep 2010 Last revised: 8 Feb 2012

See all articles by Vincenzo Aquaro

Vincenzo Aquaro

affiliation not provided to SSRN

Marco Bardoscia

Bank of England

Roberto Bellotti

University of Bari - Department of Physics; Istituto Nazionale di Fisica Nucleare (INFN) - Sezione di Bari

Arianna Consiglio

Italian National Research Council (CNR) - Institute for Biomedical Technologies (ITB)

Francesco De Carlo

affiliation not provided to SSRN

Giovanni Ferri

LUMSA University

Date Written: 2009

Abstract

A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.

Keywords: Operational Risk, Bayesian Networks, Time Series, Value at Risk, Different-Times Correlations

Suggested Citation

Aquaro, Vincenzo and Bardoscia, Marco and Bellotti, Roberto and Consiglio, Arianna and De Carlo, Francesco and Ferri, Giovanni, A Bayesian Networks Approach to Operational Risk (2009). Physica A, Vol. 389, No. 8, pp. 1721-1728, 2010. Available at SSRN: https://ssrn.com/abstract=1674078

Vincenzo Aquaro

affiliation not provided to SSRN ( email )

Marco Bardoscia (Contact Author)

Bank of England ( email )

Threadneedle Street
London, EC2R 8AH
United Kingdom

Roberto Bellotti

University of Bari - Department of Physics ( email )

Via Amendola 173
Bari, BA 70126
Italy

Istituto Nazionale di Fisica Nucleare (INFN) - Sezione di Bari

Via Amendola 173
Bari, BA 70126
Italy

Arianna Consiglio

Italian National Research Council (CNR) - Institute for Biomedical Technologies (ITB) ( email )

Via Fratelli Cervi, 93
Segrate, 20090
Italy

Francesco De Carlo

affiliation not provided to SSRN

Giovanni Ferri

LUMSA University ( email )

Via della Traspontina
Roma, Rome 00192
Italy

HOME PAGE: http://www.lumsa.it/giovanni-ferri

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