Risk models-at-risk |
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| Institution: | 1. A.A.Advisors-QCG (ABN AMRO), France;2. Variances and Univ. Lorraine (CEREFIGE), France;3. Systemic Risk Centre and London School of Economics, United Kingdom;4. Variances, Univ. La Reunion and Orleans (CEMOI, LEO/CNRS and LBI), France;1. Department of Business Administration, Universidad Carlos III, Spain;2. D.G.A. Supervisión – Banco de España, Spain;1. DEIB, Politecnico di Milano, Milano, Italy;2. DTIS, Onera, Toulouse, France;1. John Molson School of Business, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, Quebec H3G 1M8, Canada;2. Chinese Academy of Finance and Development, Central University of Finance and Economics, 39 South College Road, Haidian District, Beijing 100081, PR China;1. Department of Applied Economics, Department of Finance, National Chung Hsing University, Taichung, Taiwan;2. Instituto Complutense de Análisis Económico (ICAE), Facultad de Ciencias Económicas y Empresariales, Universidad Complutense de Madrid, Spain;3. Department of Economics, Emory University, USA;4. Department of Finance, Asia University, Taiwan;5. Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, the Netherlands;6. Discipline of Business Analytics, University of Sydney Business School, Australia;7. Institute of Advanced Sciences, Yokohama National University, Japan |
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| Abstract: | The experience from the global financial crisis has raised serious concerns about the accuracy of standard risk measures as tools for the quantification of extreme downward risks. A key reason for this is that risk measures are subject to a model risk due, e.g. to specification and estimation uncertainty. While regulators have proposed that financial institutions assess the model risk, there is no accepted approach for computing such a risk. We propose a remedy for this by a general framework for the computation of risk measures robust to model risk by empirically adjusting the imperfect risk forecasts by outcomes from backtesting frameworks, considering the desirable quality of VaR models such as the frequency, independence and magnitude of violations. We also provide a fair comparison between the main risk models using the same metric that corresponds to model risk required corrections. |
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| Keywords: | Model risk Value-at-risk Backtesting |
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