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Risk Management of Risk Under the Basel Accord: A Bayesian Approach to Forecasting Value-at-Risk of VIX Futures

Author

Listed:
  • Roberto Casarin
  • Chia-Lin Chang
  • Juan-Ángel Jiménez-Martín
  • Michael McAleer

    (University of Canterbury)

  • Teodosio Pérez Amaral

Abstract

It is well known that the Basel II Accord requires banks and other Authorized Deposit-taking Institutions (ADIs) to communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models, whether individually or as combinations, to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. McAleer et al. (2009) proposed a new approach to model selection for predicting VaR, consisting of combining alternative risk models, and comparing conservative and aggressive strategies for choosing between VaR models. This paper addresses the question of risk management of risk, namely VaR of VIX futures prices, and extends the approaches given in McAleer et al. (2009) and Chang et al. (2011) to examine how different risk management strategies performed during the 2008-09 global financial crisis (GFC). The empirical results suggest that an aggressive strategy of choosing the Supremum of single model forecasts, as compared with Bayesian and non-Bayesian combinations of models, is preferred to other alternatives, and is robust during the GFC. However, this strategy implies relatively high numbers of violations and accumulated losses, which are admissible under the Basel II Accord.

Suggested Citation

  • Roberto Casarin & Chia-Lin Chang & Juan-Ángel Jiménez-Martín & Michael McAleer & Teodosio Pérez Amaral, 2011. "Risk Management of Risk Under the Basel Accord: A Bayesian Approach to Forecasting Value-at-Risk of VIX Futures," Working Papers in Economics 11/26, University of Canterbury, Department of Economics and Finance.
  • Handle: RePEc:cbt:econwp:11/26
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    as
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    Cited by:

    1. Chia-Lin Chang & David E. Allen & Michael McAleer & Ju-Ting Tang & Teodosio Pérez Amaral, 2013. "Risk Modelling and Management: An Overview," Documentos de Trabajo del ICAE 2013-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & McAleer, Michael & Amaral, Teodosio Perez, 2013. "The rise and fall of S&P500 variance futures," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 151-167.
    3. Campos, I. & Cortazar, G. & Reyes, T., 2017. "Modeling and predicting oil VIX: Internet search volume versus traditional mariables," Energy Economics, Elsevier, vol. 66(C), pages 194-204.
    4. Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & Maasoumi, Esfandiar & McAleer, Michael & Pérez-Amaral, Teodosio, 2019. "Choosing expected shortfall over VaR in Basel III using stochastic dominance," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 95-113.
    5. Chia-Lin Chang & Juan-Ángel Jiménez-Martín & Esfandiar Maasoumi & Michael McAleer & Teodosio Pérez-Amaral, 2015. "A Stochastic Dominance Approach to the Basel III Dilemma: Expected Shortfall or VaR?," Tinbergen Institute Discussion Papers 15-056/III, Tinbergen Institute.
    6. Casarin, Roberto & Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & McAleer, Michael & Pérez-Amaral, Teodosio, 2013. "Risk management of risk under the Basel Accord: A Bayesian approach to forecasting Value-at-Risk of VIX futures," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 183-204.
    7. Kinateder, Harald & Campbell, Ross & Choudhury, Tonmoy, 2021. "Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets," Finance Research Letters, Elsevier, vol. 43(C).
    8. Chang, Chia-Lin & Jiménez-Martín, Juan-Ángel & Maasoumi, Esfandiar & Pérez-Amaral, Teodosio, 2015. "A stochastic dominance approach to financial risk management strategies," Journal of Econometrics, Elsevier, vol. 187(2), pages 472-485.
    9. Chiara Lattanzi & Manuele Leonelli, 2019. "A changepoint approach for the identification of financial extreme regimes," Papers 1902.09205, arXiv.org.
    10. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    11. Chao Wang & Qian Chen & Richard Gerlach, 2017. "Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution," Papers 1707.03715, arXiv.org.
    12. Tim Leung & Brian Ward, 2015. "The golden target: analyzing the tracking performance of leveraged gold ETFs," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(3), pages 278-297, August.
    13. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    14. Fries, Christian P. & Nigbur, Tobias & Seeger, Norman, 2017. "Displaced relative changes in historical simulation: Application to risk measures of interest rates with phases of negative rates," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 175-198.
    15. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
    16. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.

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    More about this item

    Keywords

    Median strategy; Value-at-Risk; daily capital charges; violation penalties; aggressive risk management; conservative risk management; Basel Accord; VIX futures; Bayesian strategy; quantiles; forecast densities;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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