Portfolio value at risk based on independent component analysis |
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Authors: | Ying Chen Wolfgang Härdle Vladimir Spokoiny |
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Institution: | 1. CASE—Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, Spandauerstrasse 1, 10178 Berlin, Germany;2. Weierstraß—Institute für Angewandte Analysis und Stochastik, Mohrenstrasse 39, 10117 Berlin, Germany |
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Abstract: | Risk management technology applied to high-dimensional portfolios needs simple and fast methods for calculation of value at risk (VaR). The multivariate normal framework provides a simple off-the-shelf methodology but lacks the heavy-tailed distributional properties that are observed in data. A principle component-based method (tied closely to the elliptical structure of the distribution) is therefore expected to be unsatisfactory. Here, we propose and analyze a technology that is based on independent component analysis (ICA). We study the proposed ICVaR methodology in an extensive simulation study and apply it to a high-dimensional portfolio situation. Our analysis yields very accurate VaRs. |
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Keywords: | 62G05 62H12 62H10 |
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