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As described on this link, mVaR represents an empirical expression adjusted for skewness and kurtosis of the empirical distribution. As we know, empirical returns are commonly skewed and peaked, such that assuming normal distribution is a bad fit to estimate VaR. Therefore, mVaR adjusts for skewness and kurtosis to better reflect the empirical VaR.


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Using anything with "VaR" in the name, is basically a bad idea. But a modified VaR does not assume a normal distributed random variable. So maybe that makes people feel a little better. mVaR might look equal to VaR at "high confidence levels" but it is well-known that both measures are inaccurate at high confidence levels. mVaR may even be worse, given the ...


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You can pass in the parameters are you estimating with EWMA or GARCH using the mu (mean), sigma (co/variance) m3 (co/skewness) and m4(co/kurtosis) arguments. e.g. blahblah = EWMA(my_time_series) VaR(my_time_series,mu=blahblah)


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Yes and no :-) Portfolio VaR = CV1 + CV2 + CV3 + CV4 is correct. To safeguard my answer, I looked this up from thinxlabs.com The individual component VaRs from the assets in the portfolio should add up tho the total portfolio VaR. The equation is as follows. But you need to calculate another VaR for each account, if you want to use CV on those. The ...


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I must say that I'd advise you not to use this kind of concepts if you don't really understand what VaR is and how it should be used, which seems to be the case here. In short, if you bought and sold the same amount of the same contract then obviously you are not exposed to market risk anymore. So intuitively you expect you risk (and hence your VaR) to be ...



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