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The results depend on your distribution of losses. If there is lot of departure from Normality, Cornish-Fisher VaR results will not be as accurate as GPD. But again to estimate block maxima effectively you need a large amount of data. So it is difficult to say much without looking at the data. Also, I would use the QRM package that accompanies the book, ...


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Of course this does not make sense. But the problem is not the total return index but (most likely) the range of historical values used in the calibration. In a Solvency II setting we are talking about annual VaR on the 99.5% level. As a quick reality check assume you are a well versed extreme event modeller. In fact you just need at least 5 events. ...


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Consider an instrument value $f(S_0^1, \ldots, S_0^n)$ that depends on $n$ spot levels. Let $$\overrightarrow{S}_0=[S_0^1, \ldots, S_0^n]^T$$ be an $n$-dimensional vector representing the spot levels. We can approximate the cross gamma \begin{align*} \frac{\partial^2 f\big(\overrightarrow{S}_0\big)}{\partial S_0^i \partial S_0^j} \end{align*} by a finite ...



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