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Depending of $\lambda$, pasts observations will be weighted differently, if you compute the volatility at time $t$ , the $t-1$ observation will be weighted by $(1-\lambda)*\lambda^{0}$, the $t-2$ observation by $(1-\lambda)*\lambda^{1}$ and so on so forth. For $\lambda= 0.94$ : The first observation is weighted by = $(1-0.94) * 0.94^0 =0.06%$ The second ...

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I can share how a pricing application (eg: QuantLib) calculates the VaR with Monte-Carlo. Generate a vector of independent Gaussian random numbers. A typical (and simple) implementation is Box-Muller. I prefer the inverse transform method, and I think this is also the default for QuantLib. Now, we will need to generate correlated returns. We will need a ...

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You most probably don't want to estimate the covariance of prices but rather the covariance of returns. Thus for equities you can take the return of the traded price. For bonds: if the maturity is long enough (say bigger than 2 years), then you can take the returns of traded prices. The pull to par should not be too relevant here. if the maturity is short ...

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Not sure whether your client wants "method" or "visualisation". I might be guessing that he expects you to present results similar to what's shown here https://msci.com/resources/research/articles/2015/Research_Insight_Backtesting_Risk_Models_2014_YearInReview.pdf

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I can't comment yet on the topic due to my reputation level (so I will throw an answer up) but having just done my MFE capstone research on EVT implementation for VaR. According to my advisor who was a director of a quant research group at Citi before returning to academia, not many people are doing this. My research was to start collecting data comparing ...

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