Value at Risk Computation in a Non-Stationary Setting
AbstractThis chapter recalls the main tools useful to compute Value at Risk associated with a m-dimensional portfolio. Then, the limitations of the use of these tools is explained, as soon as non-stationarities are observed in time series. Indeed, specific behaviours observed by financial assets, like volatility, jumps, explosions, and pseudo-seasonalities, provoke non-stationarities which affect the distribution function of the portfolio. Thus, a new way for computing VaR is proposed which allows the potential non-invariance of the m-dimensional portfolio distribution function to be avoided.
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Bibliographic InfoPaper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00511995.
Date of creation: 2010
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Publication status: Published, Handbook on Model Risk : Measuring, managing and mitigating model risk, lessons from financial crisis, John Wiley (Ed.), 2010, 431-454 - chapter 19
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Non-stationarity - Value-at-Risk - Dynamic copula -Meta-distribution - POT method.;
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- Dominique Guegan & Pierre-AndrÃ© Maugis, 2008.
"New prospects on vines,"
Documents de travail du Centre d'Economie de la Sorbonne, UniversitÃ© PanthÃ©on-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne
b08095, UniversitÃ© PanthÃ©on-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Mar 2010.
- Dominique Guegan & Pierre-André Maugis, 2010. "New Prospects on Vines," UniversitÃ© Paris1 PanthÃ©on-Sorbonne (Post-Print and Working Papers) halshs-00348884, HAL.
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