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In my understanding that truly depends on how large the time frame you consider is. Indeed, on one side you have log-returns on the other conditional volatility, which are two different concepts.


The qdist function turned out to be standardized and so the VaR function is: f <- function(x, mu, sigma, skew, shape){ return(qdist("nig", p=x, mu =mu , sigma = sigma, skew=skew, shape=shape)) } Making this adjustment to the f function and then proceeding the same way you will obtain a good graph for the CVaR es_nig <- function(p = 0.05){ mu &...


Why would you need to model volatility to test an hypothesis. Just use the historical realised volatility and if you want to test the hypothesis how funds relate in the near future, then use the VIX index, it's a forward looking measure. Or you use some volatility tracking fund as a proxy, why use some model to estimate relationships when obviously some ...

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