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Try modelling samples every 20,000 ticks, instead of 2 hours (or any such number like that). Markets are often less fat tailed in terms of the trade- or volume-clock. See http://www.amazon.ca/Introduction-High-Frequency-Finance-Ramazan-Gen%C3%A7ay/dp/0122796713 and http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2034858


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Have you looked at the PerformanceAnalytics R package functions? It should allow you to calculate delta normal Var quite easily. I recommend you look at the instructions manual but here is the code for it: VaR(R = NULL, p = 0.95, ..., method = c("modified", "gaussian", "historical", "kernel"), clean = c("none", "boudt", "geltner"), portfolio_method = ...


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the answer is simple: look at key differences between these two models. GBM is diffusion, OU is mean-reversion


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Given efficient markets, asset prices should be unpredictable in the sense that any upcoming returns are uncorrelated with current or past returns. Hence for traded assets the price should follow something more similar to a GBM than an O-U process. However, many financial metrics are not prices; for example interest rates or volatility. O-U processes may ...



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