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Addressing the Data Truncation Problem

In: Modelling Operational Risk Using Bayesian Inference

Author

Listed:
  • Pavel V. Shevchenko

    (CSIRO, Mathematics, Informatics and Statistics)

Abstract

Typically, operational risk losses are reported above some threshold. This chapter studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution. Fitting data reported above a constant threshold is a well-known and studied problem. However, in practice, the losses are scaled for business and other factors before the fitting and thus the threshold varies across the scaled data sample. The reporting level may also change when a bank changes its reporting policy. This chapter considers the issue of thresholds – both constant and time-varying. The maximum likelihood and Bayesian Markov chain Monte Carlo approaches to fit the models are discussed.

Suggested Citation

  • Pavel V. Shevchenko, 2011. "Addressing the Data Truncation Problem," Springer Books, in: Modelling Operational Risk Using Bayesian Inference, chapter 0, pages 179-201, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-15923-7_5
    DOI: 10.1007/978-3-642-15923-7_5
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