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Value at Risk and Expected Shortfall under General Semi-parametric GARCH models

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  • Xuehai Zhang

    (Paderborn University)

Abstract

Risk management has been emphasized by financial institutions and the Basel Com- mittee on Banking Supervision (BCBS). The core issue in risk management is the mea- surement of the risks. Value at Risk (VaR) and Expected Shortfall (ES) are the widely used tools in quantitative risk management. Due to the ineptitude of VaR on tail risk performances, ES is recommended as the financial risk management metrics by BCBS. In this paper, we generate general SemiGARCH class models with a time-varying scale function. GARCH class models, based on the conditional t-distribution, are parametric extensions. Besides, backtesting with the semiparametric approach is also discussed. Fol- lowing Basel III, the trac light tests are applied in the model validation. Finally, we propose the loss functions with the views from regulators and firms, combing a power transformation in the model selection and it is shown that semiparametric models are a necessary option in practical financial risk management.

Suggested Citation

  • Xuehai Zhang, 2019. "Value at Risk and Expected Shortfall under General Semi-parametric GARCH models," Working Papers CIE 126, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:126
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    References listed on IDEAS

    as
    1. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 122, Paderborn University, CIE Center for International Economics.
    2. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    3. Christoffersen, Peter, 2011. "Elements of Financial Risk Management," Elsevier Monographs, Elsevier, edition 2, number 9780123744487.
    4. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    5. Susan Thomas & Mandira Sarma & Ajay Shah, 2003. "Selection of Value-at-Risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 337-358.
    6. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 125, Paderborn University, CIE Center for International Economics.
    7. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    8. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    9. Nick Costanzino & Michael Curran, 2018. "A Simple Traffic Light Approach to Backtesting Expected Shortfall," Risks, MDPI, vol. 6(1), pages 1-7, January.
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