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Bayesian Value-at-Risk with Product Partition Models
Year Of Publication: 2009
Month Of Publication: May
Pages: 30
Download Count: 9
View Count: 1561
Comment Num: 0
Language: English
Who Can Read: Free
Date: 5-30-2010
Publisher: Administrator
Summary
In this paper we propose a novel Bayesian methodology for Value-at-Risk compu-
tation based on parametric Product Partition Models. Value-at-Risk is a standard
tool to measure and control the market risk of an asset or a portfolio, and it is also
required for regulatory purposes. Its popularity is partly due to the fact that it is an
easily understood measure of risk. The use of Product Partition Models allows us
to remain in a Normal setting even in presence of outlying points, and to obtain a
closed-form expression for Value-at-Risk computation. We present and compare two
different scenarios: a product partition structure on the vector of means and a prod-
uct partition structure on the vector of variances. We apply our methodology to an
Italian stock market data set from Mib30. The numerical results clearly show that
Product Partition Models can be successfully exploited in order to quantify mar-
ket risk exposure. The obtained Value-at-Risk estimates are in full agreemen
Author(s)
Bormetti, Giacomo Sign in to follow this author
De Giuli, Maria Elena Sign in to follow this author
Delpini, Danilo Sign in to follow this author
Tarantola, Claudia Sign in to follow this author
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