In this paper we deal with the use of multivariate normal mixture distributions to model asset returns, In particular, by modelling daily asset returns as a mixture of a low-volatility and a high-volatility distribution, we obtain three main results: (i) we can use posterior probabilities to identify hectic observations; (ii) we are able to compute a non-parametric fat-tails Value at Risk by sampling repeatedly from the mixture and computing the quantile of the empirical distribution; (iii) we can use the estimated parameters of the hectic distribution for stress testing purposes. We show how these three items can be addressed using either real data and simulation methods.
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Paper provided by Department of Computer and Management Sciences, University of Trento, Italy in its series Alea Tech Reports with number
012.
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