Operational Risk Assessment Using Bayesian Inference with Regard to the Composition of Data Sources and the Assumption of Dependence between Experts and Internal Loss Data

Document Type : Research Paper

Authors

1 Assistant Professor of Industrial Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran,

2 MSc student of Financial Engineering, Tarbiat Modares University

3 Assistant Professor of Industrial Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University

Abstract

In order to measure hedge funds operating under the wings of two documented, many financial institutions tend to use the loss distribution approach. But a loss distribution approach requires a large number of internal loss data in order to have the necessary performance, so due to limitations in the database operating losses and the cost of internal loss data collection, in order to increase performance and reliability the operational risk capital should be calculated from other data sources used for operational risk. The biggest challenge facing financial institutions is how to combine different data sources of operational risk. In this regard, expressed in this research has been how to combine a variety of data source. So, in this paper the parameter estimation of frequency of operational risk loss distribution approach using Bayesian inference is explored. In this research, assuming dependencies between data sources, operational risk, the experts and internal loss data is intended. To validate the estimated models for the posterior distribution of numerical tests of goodness of fit is used. In addition,  to calculate dependencies between data sources, detailed functions family of Gauss is used. The results indicate that with the assumption of experts dependence between the source data and internal data loss, by increasing the number of predictive parameters, frequency distribution, reduced the value of the parameter distribution, which represents a decrease of profile risk over time.

Keywords

References

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History

  • Receive Date: 14 September 2017
  • Revise Date: 29 March 2018
  • Accept Date: 06 May 2018

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