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Forecasting Portfolio-Value-at-Risk with Nonparametric Lower Tail Dependence Estimates
Company: Technische Universität Dortmund Fakultät Mathematik
Company Url: Click here to open
Year Of Publication: 2013
Month Of Publication: April
Pages: 32
Download Count: 2
View Count: 1247
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 5-4-2013
Publisher: Administrator
Summary
We propose to forecast the Value-at-Risk of bivariate portfolios using copulas which are calibrated on the basis of nonparametric sample estimates of the coef?cient of lower tail dependence. We compare our proposed method to a conventional copula-GARCH model where the parameter of a Clayton copula is estimated via Canonical Maximum-Likelihood. The superiority of our proposed model is exempli?ed by analyzing a data sample of nine different ?nancial portfolios. A comparison of the out-of-sample forecasting accuracy of both models con?rms that our model yields economically signi?cantly better Value-at-Risk forecasts than the competing parametric calibration strategy.
Author(s)
Weiss, Gregor N. Sign in to follow this author
Silburg, Karl F. Sign in to follow this author
Stoimenov, Pavel Sign in to follow this author
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