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4

Yes, it is correct. Underestimation: you under-estimate the risk, so you have more VaR violations than what your model predicts. Ex: With 100 observations, and a 99% VaR, you expect 1 violation but you observe 5 violations. Overestimation: you over-estimate the risk, i.e the risk is less important that you expect. You observe less VaR violations that you ...


0

There are a number of different ways to accomplish your goal. One would involve modelling each financial time series and then connecting these marginal distributions using a copula. Monte Carlo is then a matter of simulating the marginals and the copula. In using your Cholesky matrix, you are implicitly using an elliptical distribution (think of Gaussian ...


2

Have you considered Marginal Contribution to Total risk (MCTR)? You can decompose your risk across securities/sub-sectors/sectors, such that sum(weight of security * MCTR of security ) = portfolio risk (standard deviation). A good discussion on the topic can be found in Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and ...



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