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Using Conditional Copula to Estimate Value at Risk
Company: Journal of Data Science
Year Of Publication: 2006
Month Of Publication: January
Pages: 93-115
Download Count: 907
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Comment Num: 0
Language: EN
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Who Can Read: Free
Date: 2-5-2006
Publisher: Administrator
Summary
Value at Risk (VaR) plays a central role in risk management.There are several approaches for the estimation of VaR, such as historicalsimulation, the variance-covariance (also known as analytical), and theMonte Carlo approaches. Whereas the first approach does not assume anydistribution, the last two approaches demand the joint distribution to beknown, which in the analytical approach is frequently the normal distribution.The copula theory is a fundamental tool in modeling multivariatedistributions. It allows the definition of the joint distribution through themarginal distributions and the dependence between the variables. Recentlythe copula theory has been extended to the conditional case, allowing the useof copulae to model dynamical structures. Time variation in the first andsecond conditional moments is widely discussed in the literature, so allowingthe time variation in the conditional dependence seems to be natural.This work presents some concepts and properties of copula functions andan application of the copula theory in the estimation of VaR of a portfoliocomposed by Nasdaq and S&P500 stock indices.
Author(s)
Palaro, Helder P. Sign in to follow this author
Hotta, Luiz Koodi Sign in to follow this author
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