We describe a numerical procedure to obtain bounds on the distribution function of a sum of n dependent risks having fixed marginals. With respect to the existing literature, our method provides improved bounds and can be applied also to large non-homogeneous portfolios of risks. As an application, we compute the VaR-based minimum capital requirement for a portfolio of operational risk losses.

Appendix A Deriving Equations for M+ME Biases and for t-statistic Overestimations Determining the Impact of VIF Bias on the t-statistic Consider first the situation where we have no measurement error. Using standard equations for the estimated standard error of β2 from any regression textbook, an unbiased and consistent estimate of the standard error is s = Σεi / (N – K) (A1-1) and the estimate...

Value at Risk (VaR) is a relatively new methodology used to quantify risk exposure. Although widely used in the financial and energy sectors of the economy, VaR has yet to gain the same acceptance in the field of agriculture. This thesis provides an introduction to Value at Risk and explains both its strengths and weaknesses. Empirical

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In this paper, following the generalization of Delta Normal VaR to Delta Mixture Elliptic VaR in Sadefo-Kamdem [3], we give and explicit formula to estimate linear VaR and ES when the risk factors changes with the mixture of t-Student distributions. In particular, we give rise to Delta-Mixture-Student VaR and the Delta-Mixture-Elliptic ES.

Generally, in the financial literature, the notion of quadratic VaR is implicitly confused with the Delta-Gamma VaR, because more authors dealt with portfolios that contained derivatives instruments. In this paper, we postpone to estimate both the expected shortfall and Value-at-Risk of a quadratic portfolio of securities (i.e equities) without the Delta and Gamma Greeks, when the joint log-ret...

The conditional tail expectation in risk analysis describes the expected amount of risk that can be experienced given that a potential risk exceeds a threshold value, and provides an important measure for right-tail risk. In this paper, we study the convolution and extreme values of dependent risks that follow a multivariate phase type distribution, and derive explicit formulas of several condi...

This paper compares three value-at-risk approximation methods suggested in the literature: Cornish-Fisher (1937), Sillitto (1969), and Liu (2010). Simulation results are obtained for three families of distributions: student-t, skewed-normal, and skewed-t. We recommend the Sillitto approximation as the best method to evaluate the value at risk when the financial return has an unknown, skewed, an...

rule ClassWithOperationsContributingClass merge c : Core!EClass with s : ObserverPattern!EClass into t : Target!EClass { for (sop in s.eOperations) { var op : new Target!EOperation; op.name := sop.name; t.eOperations.add(op); for (sopp in sop.eParameters) { var p : new Target!EParameter; p.name := sopp.name; p.eType ::= sopp.eType; op.eParameters.add(p); } } } auto rule ClassWithObserver merge ...