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1

There exist a lot of way to choose risk factors and the choice differs according to the kind of underlying assets. In your case, particularly, since the portfolio is composed by currencies, I would choose the risk factors mainly among all the macroeconomic variables available in your dataset or data provider. After that, to choose on which of them basing ...


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Yes, it exists and it is called ccgarch package. You can install that by simply running in R install.packages("ccgarch") and learn more about that on the CRAN relative paper. Moreover, I suggest you to read this lecture hold by the author during an R conference. Hope this help.


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so i had to do a couple changes to get it fully working for me, but Rohit you pretty much got it close. Again this is what worked in my env fractalindicator.up <- function(x) { x$FUp <- 0 High <- Hi(x); Bars <- nrow(x) for(iBar in seq(8,Bars-2)) { if(High[[iBar-1]]<High[[iBar-2]] && High[[iBar]]<High[[iBar-2]]) ...


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Most technical indicators must be available in the TTR package. However, if they are not then you can write a custom indicator for use in quantstrat as follows. fractalindicator.up <- function(x) { High <- Hi(x); Bars <- nrow(x) afFrUp <- rep(NA, Bars) for(iBar in seq(8,Bars-2)) { if(High[iBar-1]<High[iBar-2] && ...


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Answering my own question as it could be useful for others. Actually package fOptions is vectorized. The only constraint (and that make sense) is that you can't compute at the same time 2 different greeks, or mix up calls and puts. So assuming that you want to compute the delta of a set of puts, the code will be the following: ...


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if you put all your option objects into a list then you can use lapply. Read the documentation or just thist post for details.


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Few comments on your questions: 1) Yes, Arch and Garch are suitable for equities volatility, please see: http://onlinelibrary.wiley.com/doi/10.1002/jae.800/pdf 2) No. These are models of volatility. To model interest rates use CIR, Vasicek or similar. 3) and 4) Check paper above.


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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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For non-normal asset price models you could look at the theory of Lévy-processes. If we assume that you work in the physical probability measure $P$ and that the random numbers that you have generated are daily log-returns, then you can do the following: Asset $i$ has starting price $S_0^i$ and for the future prices you can put $$ S_t^i = S_0^i ...



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