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Value at Risk Estimation

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Handbook of Computational Finance

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

This chapter reviews the recent developments of Value at Risk (VaR) estimation. In this survey, the most available univariate and multivariate methods are presented. The robustness and accuracy of these estimation methods are investigated based on the simulated and real data. In the backtesting procedure, the conditional coverage test (Christoffersen, Int. Econ. Rev. 39:841–862, 1998), the dynamic quantile test (Engle and Manganelli, J. Bus. Econ. Stat. 22(4):367–381, 2004) and Ljung-Box test (Berkowitz and O’Brien, J. Finance 57(3):1093–1111, 2002) are used to justify the performance of the methods.

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Authors and Affiliations

  1. Department of Statistics and Applied Probability, National University of Singapore, Singapore, 117546, Singapore

    Ying Chen & Jun Lu

Authors
  1. Ying Chen
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  2. Jun Lu
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Correspondence to Jun Lu .

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Editors and Affiliations

  1. , Risk Management Institute, National University of Singapore, 21 Heng Mui Keng Terrace, Level 4, Singapore, 119613, Singapore

    Jin-Chuan Duan

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. , Department of Computational and Data Sci, George Mason University, University Drive 4400, Fairfax, 22030, Virginia, USA

    James E. Gentle

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Chen, Y., Lu, J. (2012). Value at Risk Estimation. In: Duan, JC., Härdle, W., Gentle, J. (eds) Handbook of Computational Finance. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17254-0_12

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