Mixture models for VaR and stress testing
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.
|Date of creation:||Jun 2001|
|Date of revision:||14 Jun 2008|
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- Alessandro Beber, 2001.
"Determinants of the implied volatility function on the Italian Stock Market,"
LEM Papers Series
2001/05, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Alessandro Beber, 2001. "Determinants of the implied volatility function on the Italian Stock Market," Alea Tech Reports 010, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
- Luca Erzegovesi, 1999. "Rischio e incertezza in finanza: classificazione e logiche di gestione," Alea Tech Reports 006, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
- Benoit Mandelbrot, 2015.
"The Variation of Certain Speculative Prices,"
World Scientific Book Chapters,
in: THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78
World Scientific Publishing Co. Pte. Ltd..
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