Financial Risk Measures Using Dynamic Copulas: A Semiparametric Approach
Company: Colombia Banco de la Republica
Company Url: Click here to open
Year Of Publication: 2009
Month Of Publication: April
Pages: 25
Download Count: 20
View Count: 1732
Comment Num: 0
Language: English
Who Can Read: Free
Date: 4-17-2010
Publisher: Administrator
Summary
In this paper we use a semiparametric dynamic copula approach to estimate
Value at Risk (VaR) and Expected Shortfall (ES). First, VAR-GARCH models are used in
order to deal with temporal dependence. Second, in contrast to previous works which assume
parametric distribution functions, marginal distributions of the standardized residuals
of the first step models are estimated by a semiparametric approach using empirical
distributions and Extreme Value Theory. Third, copula distributions are estimated on the
basis of uniform random variables obtained in the second step. Finally, VaR and ES are
estimated through Monte Carlo simulations. This methodology could be useful for portfolios
with a large number of assets, because the identification process of the univariate
marginal distributions is easier, and additionally, this methodology reduces the model risk
derived from selecting an ex-ante specific distribution.
As an application of this methodology, VaR and ES are estimated for
Value at Risk (VaR) and Expected Shortfall (ES). First, VAR-GARCH models are used in
order to deal with temporal dependence. Second, in contrast to previous works which assume
parametric distribution functions, marginal distributions of the standardized residuals
of the first step models are estimated by a semiparametric approach using empirical
distributions and Extreme Value Theory. Third, copula distributions are estimated on the
basis of uniform random variables obtained in the second step. Finally, VaR and ES are
estimated through Monte Carlo simulations. This methodology could be useful for portfolios
with a large number of assets, because the identification process of the univariate
marginal distributions is easier, and additionally, this methodology reduces the model risk
derived from selecting an ex-ante specific distribution.
As an application of this methodology, VaR and ES are estimated for
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