M C S THE definit ion of the different sectors of the indus t r ia l structure of an economy has to be baaed on a p ragmat ic appraisal of the preva i l ing conditions, w i t h due al lowance for adjustments and var ia t ions . if it is to be economical ly meaningful . Such an approach is needed a l l the more in the context of a g r o w i n g economy, fast developing as a result of conscious a...

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.