By Greg N. Gregoriou (eds.)
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The Appendix summarizes the various steps of this algorithm. 1 Selection of a threshold A sound loss data collection process is key to operational risk management and measurement as statistical inference based on historical internal loss data and monitoring/reporting activities both heavily rely on the quality of the collected data. Coherence and completeness of collected data amongst business units is therefore crucial. Selecting the most adequate collection threshold is obviously bankspeciﬁc, as each bank will examine the tradeoff between increasing the number of observations in its internal database and the associated increase in costs.
4, one possibility simply has the rebalancing cost for each asset being equal to its price. This special case minimizes the dollar-denominated amount of rebalancing. However, such a formulation is not necessarily compatible with our objective of including derivatives or other (leveraged) assets A M I Y A T O S H P U R N A N A N D A M E T A L. 27 capable of offering negative correlation. 3. 4, an enhanced methodology which accounts for ﬁrm preferences also becomes explicit, and addresses the second issue.
Preliminary analysis indicates that frequencies of both samples are well described by a Poisson process. As this distribution is characterized by a single parameter which is the average of the observed frequency, we use a Poisson (1666) and a Poisson (7841) to model frequency for Business Line 1 (hereafter BL1) and Business Line 2 (hereafter BL2), respectively. To model severity, we start by applying a single PDF for the whole distribution. 07), respectively. But as often encountered with operational risk losses, even these distributions are unable to satisfactorily capture the whole distributional form, especially at the tail level.
Advances in Risk Management by Greg N. Gregoriou (eds.)