By Greg N. Gregoriou

This publication contains an edited sequence of papers approximately threat administration and the most recent advancements within the box. overlaying issues akin to Stochastic Volatility, threat Dynamics, climate Derivatives and Portfolio Diversification, this publication may have vast foreign charm. it's hugely relevany for optimum portfolio allocation for either deepest and institutional traders around the world.

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2 contains additional economic motivation in favor of rebalancing. A M I Y A T O S H P U R N A N A N D A M E T A L. 33 To clarify, portfolio theory selects portfolio weights to exploit diversification before choosing the desired amount of riskfree capital. These decisions are independent and sequential since the risky portfolio is assumed to already be fully diversified. However, this assumption is not present in our methodology. As demonstrated in the next section, we recognize that a fully diversified portfolio results in our risk measure being identical to that of ADEH.

6 For banks that are members of a data collection consortium, the decision is sometimes driven by the rules of the consortium: The Italian initiative DIPO led by the ABI (the Italian Bankers’ Association) requires banks to provide all their operational risk losses above a threshold fixed at 5,000 EUR. ORX is a private consortium comprising large internationally active banks. It has fixed the reporting threshold at 20,000 EUR. Y V E S C R A M A, G E O R G E S H Ü B N E R A N D J E A N- P H I L I P P E P E T E R S 9 For smaller banks, however, fixing a threshold at 10,000 EUR might drastically reduce the amount of data available for computing the capital requirements.

2001) “Loss Distribution Approach for Operational Risk”, Working Paper, Groupe de Recherche Opérationnelle, Crédit Lyonnais. , Moudoulaud, O. and Roncalli, T. (2003) “Loss Distribution Approach in Practice”, Working Paper, Groupe de Recherche Opérationnelle, Crédit Lyonnais. J. (1958) Statistics of Extremes (New York: Columbia University Press). J. and Palm, F. (2001) “Tail-Index Estimates in Small Samples”, Journal of Business and Economic Statistics, 19(1): 208–16. H. E. (1998) Loss Models – From Data to Decisions (New York: Wiley Series on Probability and Statistics).

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