By Greg N. Gregoriou
This e-book contains an edited sequence of papers approximately hazard administration and the newest advancements within the box. protecting subject matters similar to Stochastic Volatility, probability Dynamics, climate Derivatives and Portfolio Diversification, this ebook could have extensive foreign attraction. it's hugely relevany for optimum portfolio allocation for either inner most and institutional traders all over the world.
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Additional resources for Advances in Risk Management (Finance and Capital Markets)
Thus, they are averse to adding riskfree capital to their portfolio for performance considerations, yet are constrained by an external regulator. Balancing the demands of an external regulator and the performance objectives of ﬁrms is accomplished by introducing portfolio theory into the measurement of risk. Speciﬁcally, our proposed risk measure offers ﬁrms the ability to rebalance their portfolio. During this rebalancing, the addition of riskfree capital remains feasible, but is not the exclusive means by which a portfolio becomes acceptable.
First, the level of the collection threshold has little impact on regulatory capital charge estimations. The own funds needed to cover operational risk are indeed stable in both business lines. 43 million depending on the threshold. 85 million. Second, this result is mainly due to the way the tail is modeled. As we rely on EVT to model the very high losses, the collection threshold has no or little impact on the fatness of the tail for the severity distribution. Finally, the choice of the collection threshold should thus not be guided by capital requirements concerns but rather by a “pro/cons” analysis of the practical implementation issues (costs, required systems, resources…) as regulatory arbitrage seems not to be applicable in this case.
For both business lines, we consider all the observations above 100 to be potential threshold candidates. This means that m = 59 for BL1 and m = 227 for BL2. 4. 4 reports the estimation of the three parameters for the GPD. 9 The next step is to model the “body” of the distribution, for example, the losses that are below the estimated extreme threshold. 25 in this case) and 375. 25 and 450. 1 and we use the Cramer– von Mises statistic as a discriminant factor to compare goodness of the ﬁt. Once the severity is fully characterized, 10,000 Monte Carlo simulations are performed to derive the aggregate loss distribution for each business line.
Advances in Risk Management (Finance and Capital Markets) by Greg N. Gregoriou