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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 &...


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weeklyReturnis for univariate series. Try this tail(do.call("merge",apply(tempPort,2,weeklyReturn,type = 'log', leading=TRUE))) # weekly.returns weekly.returns.1 # 2019-04-18 0.006692682 -0.00458900 # 2019-04-26 -0.132748543 0.07148027 # 2019-05-03 -0.034230826 0.02057131 # 2019-05-10 -0.051856081 -0.03715810 # ...


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From my own experience, DolphinDB is a pretty good substitute for kdb+. The speed of DolphinDB is actually faster than kdb+ in many cases, for examples, asof join, window join and rolling window calculations. Also, the scripting language of DolphinDB is SO easy to use. It's mostly SQL, but also include advanced features, such as functional programming. ...


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