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- handle: 10670/1.82qm9o
- halshs: halshs-01491990
info:eu-repo/semantics/OpenAccess
Mots-clés
Risks Multimodal distributions Value-at-Risk Expected Shortfall Moments method Adapted rejection sampling Regulation C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C13 - Estimation: General C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C15 - Statistical Simulation Methods: General G - Financial Economics/G.G2 - Financial Institutions and Services/G.G2.G28 - Government Policy and Regulation G - Financial Economics/G.G3 - Corporate Finance and Governance/G.G3.G32 - Financing Policy • Financial Risk and Risk Management • Capital and Ownership Structure • Value of Firms • GoodwillSujets proches
Family Family structure Families--Social aspects Families--Social conditions Family life Structure, Family Family relationships Relationships, FamilyCiter ce document
Dominique Guegan et al., « Impact of multimodality of distributions on VaR and ES calculations », HAL-SHS : économie et finance, ID : 10670/1.82qm9o
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Résumé
Unimodal probability distribution has been widely used for Value-at-Risk (VaR) computation by investors, risk managers and regulators. However, financial data may be characterized by distributions having more than one modes. Using a unimodal distribution may lead to bias for risk measure computation. In this paper, we discuss the influence of using multimodal distributions on VaR and Expected Shortfall (ES) calculation. Two multimodal distribution families are considered: Cobb's family and distortion family. We provide two ways to compute the VaR and the ES for them: an adapted rejection sampling technique for Cobb's family and an inversion approach for distortion family. For empirical study, two data sets are considered: a daily data set concerning operational risk and a three month scenario of market portfolio return built five minutes intraday data. With a complete spectrum of confidence levels from 0001 to 0.999, we analyze the VaR and the ES to see the interest of using multimodal distribution instead of unimodal distribution.