Addressing the Data Truncation Problem

Pavel V. Shevchenko ()
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Pavel V. Shevchenko: CSIRO, Mathematics, Informatics and Statistics

Chapter Chapter 5 in Modelling Operational Risk Using Bayesian Inference, 2011, pp 179-201 from Springer

Abstract: 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.

Keywords: Predictive Distribution; Joint Density; Annual Loss; Probability Generate Function; Annual Count (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-15923-7_5

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DOI: 10.1007/978-3-642-15923-7_5

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