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Estimation Risk Effects on Backtesting for Parametric Value-at-Risk Models
Year Of Publication: 2007
Month Of Publication: March
Pages: 39
Download Count: 302
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Comment Num: 0
Language: EN
Who Can Read: Free
Date: 4-4-2007
Publisher: Administrator
One of the implications of the creation of Basel Committee on Banking Supervision wasthe implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk.Thereby the correct specification of parametric VaR models became of crucial importance inorder to provide accurate and reliable risk measures. If the underlying risk model is not correctly specified, VaR estimates understate/overstate risk exposure. This can have dramaticconsequences on stability and reputation of financial institutions or lead to sub-optimal capitalallocation. We show that the use of the standard unconditional backtesting procedures to assess VaR models is completely misleading. These tests do not consider the impact of estimationrisk and therefore use wrong critical values to assess market risk. The purpose of this paperis to quantify such estimation risk in a very general class of dynamic parametric VaR models and to correct standard backtesting procedures to provide valid inference in specificationanalyses. A Monte Carlo study illustrates our theoretical findings in finite-samples. Finally,an application to S&P500 Index shows the importance of this correction and its impact oncapital requirements as imposed by Basel Accord, and on the choice of dynamic parametricmodels for risk management.
Escanciano, J. Carlos Sign in to follow this author
Olmo, Jose Sign in to follow this author
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