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Applying Robust Methods to Operational Risk Modeling
Company: Journal of Operational Risk
Company Url: Click here to open
Year Of Publication: 2006
Month Of Publication: Spring
Pages: 27-41
Download Count: 612
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Comment Num: 0
Language: EN
Source:
Who Can Read: Free
Date: 11-12-2006
Publisher: Administrator
Summary
We use robust statistical methods to analyze operational loss data. Commonlyused classical estimators of model parameters may be sub-optimal under minordepartures of data from the model assumptions. Operational loss data arecharacterized by a very heavy right tail of the loss distribution attributed toseveral “low frequency/high severity” events. Classical estimators may producebiased estimates of parameters leading to unreasonably high estimates of mean,variance and the operational risk VAR and CVAR measures. The main objectiveof robust methods is to focus the analysis on the fundamental properties ofthe bulk of the data, without being distorted by outliers. We argue that furthercomparison of results obtained under the classical and robust procedures canserve as a basis for the VAR sensitivity analysis and can lead to an understandingof the economic role played by these extreme events. An empirical study with1980–2002 public operational loss data reveals that the highest 5% of lossesaccount for up to 76% of the operational risk capital char
Author(s)
Chernobai, Anna Sign in to follow this author
Rachev, Svetlozar Sign in to follow this author
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