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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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The most common approach is to multiply by sqrt of 250. This is the standard. Although very basic. A much better solution is to make your monte carlo simulation on a 1 year time period using scaled parameters over 1 year.


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It depends on the method by which you calculate VaR. Some models (t-distributuion, normal) lead to a form of VaR such that it is just scaled volatility: $$ VaR = c \sigma $$ with some proper $c$ (e.g. $q_{\alpha}$ in the case of normal, bit more complicated for the t-distribution). Then as $\sigma$ scales with square-root-of-time so does VaR. If VaR is ...


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The most commonly used approach is multiplication by the square-root of T, 19.1 in this case. This assumes no autocorrelation, however (Markov process). Interest rates tend to show a mean reversion, so the number would be smaller than 19.1. Other cases could show the oppoite effect if there are positive feedbacks. In both of these cases, a simple time ...


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The standard approach is to multiply by the square root of the number of trading days in a year. If you assume there are 250 trading days in the year, you multiply by $\sqrt{250}$. Investopedia is one source explaining this approach.



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