IDEAS home Printed from https://ideas.repec.org/p/kyo/wpaper/761.html
   My bibliography  Save this paper

Risk Management of Risk under the Basel Accord: Forecasting Value-at-Risk of VIX Futures

Author

Listed:
  • Chia-Lin Chang

    (Department of Applied Economics, Department of Finance, National Chung Hsing University)

  • Juan-Ángel Jiménez-Martín

    (Department of Quantitative Economics, Complutense University of Madrid)

  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

  • Teodosio Pérez-Amaral

    (Department of Quantitative Economics, Complutense University of Madrid)

Abstract

The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models 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, Jimenez-Martin and Perez- Amaral (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. We examine how different risk management strategies performed during the 2008-09 global financial crisis (GFC). We find that an aggressive strategy of choosing the Supremum of the single model forecasts is preferred to the other alternatives, and is robust during the GFC. However, this strategy implies relatively high numbers of violations and accumulated losses, though these are admissible under the Basel II Accord.

Suggested Citation

  • Chia-Lin Chang & Juan-Ángel Jiménez-Martín & Michael McAleer & Teodosio Pérez-Amaral, 2011. "Risk Management of Risk under the Basel Accord: Forecasting Value-at-Risk of VIX Futures," KIER Working Papers 761, Kyoto University, Institute of Economic Research.
  • Handle: RePEc:kyo:wpaper:761
    as

    Download full text from publisher

    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP761.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Massimiliano Caporin & Michael McAleer, 2010. "The Ten Commandments For Managing Investments," Journal of Economic Surveys, Wiley Blackwell, vol. 24(1), pages 196-200, February.
    2. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(02), pages 280-310, April.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Juan-Ángel Jiménez-Martín & Michael McAleer & Teodosio Pérez-Amaral, 2009. "The Ten Commandments For Managing Value At Risk Under The Basel Ii Accord," Journal of Economic Surveys, Wiley Blackwell, vol. 23(5), pages 850-855, December.
    5. McAleer, Michael & Jimenez-Martin, Juan-Angel & Perez-Amaral, Teodosio, 2013. "GFC-robust risk management strategies under the Basel Accord," International Review of Economics & Finance, Elsevier, pages 97-111.
    6. Ling, Shiqing & McAleer, Michael, 2002. "NECESSARY AND SUFFICIENT MOMENT CONDITIONS FOR THE GARCH(r,s) AND ASYMMETRIC POWER GARCH(r,s) MODELS," Econometric Theory, Cambridge University Press, vol. 18(03), pages 722-729, June.
    7. Shiqing Ling & Michael McAleer, 2001. "On Adaptive Estimation in Nonstationary ARMA Models with GARCH Errors," ISER Discussion Paper 0548, Institute of Social and Economic Research, Osaka University.
    8. Michael McAleer & Bernardo da Veiga, 2008. "Single-index and portfolio models for forecasting value-at-risk thresholds," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 217-235.
    9. Pérignon, Christophe & Deng, Zi Yin & Wang, Zhi Jun, 2008. "Do banks overstate their Value-at-Risk?," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 783-794, May.
    10. Michael Mcaleer & Bernardo da Veiga, 2008. "Forecasting value-at-risk with a parsimonious portfolio spillover GARCH (PS-GARCH) model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 1-19.
    11. McAleer, Michael & Wiphatthanananthakul, Chatayan, 2010. "A simple expected volatility (SEV) index: Application to SET50 index options," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(10), pages 2079-2090.
    12. Michael McAleer & Juan-Angel Jimenez-Martin & Teodosio Pérez-Amaral, 0000. "Has the Basel II Accord Encouraged Risk Management during the 2008-09 Financial Crisis?," Tinbergen Institute Discussion Papers 09-039/4, Tinbergen Institute.
    13. McAleer, M.J. & Jiménez-Martín, J.A. & Pérez-Amaral, T., 2008. "A decision rule to minimize daily capital charges in forecasting value-at-risk," Econometric Institute Research Papers EI 2008-34, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Massimiliano Caporin & Michael McAleer, 2010. "Model Selection and Testing of Conditional and Stochastic Volatility Models," KIER Working Papers 724, Kyoto University, Institute of Economic Research.
    15. Li, W K & Ling, Shiqing & McAleer, Michael, 2002. " Recent Theoretical Results for Time Series Models with GARCH Errors," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 245-269, July.
    16. Michael McAleer & Juan‐Ángel Jiménez‐Martín & Teodosio Pérez‐Amaral, 2013. "International Evidence on GFC‐Robust Forecasts for Risk Management under the Basel Accord," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(3), pages 267-288, April.
    17. Ling, Shiqing & McAleer, Michael, 2002. "Stationarity and the existence of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 109-117, January.
    18. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    19. McAleer, Michael & Chan, Felix & Marinova, Dora, 2007. "An econometric analysis of asymmetric volatility: Theory and application to patents," Journal of Econometrics, Elsevier, vol. 139(2), pages 259-284, August.
    20. Michael McAleer, 2009. "The Ten Commandments For Optimizing Value-At-Risk And Daily Capital Charges," Journal of Economic Surveys, Wiley Blackwell, vol. 23(5), pages 831-849, December.
    21. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
    22. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Tim Leung & Brian Ward, 2015. "The golden target: analyzing the tracking performance of leveraged gold ETFs," Studies in Economics and Finance, Emerald Group Publishing, vol. 32(3), pages 278-297, August.
    4. 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.
    5. Chia-Lin Chang & Juan-Ángel Jiménez-Martín & Michael McAleer & Teodosio Pérez-Amaral, 2011. "The Rise and Fall of S&P500 Variance Futures," KIER Working Papers 795, Kyoto University, Institute of Economic Research.
    6. repec:eee:eneeco:v:66:y:2017:i:c:p:194-204 is not listed on IDEAS
    7. 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.
    8. 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.
    9. Chia-Lin Chang & Juan-Ángel Jiménez-Martín & Esfandiar Maasoumi & Michel McAleer & Teodosio Pérez-Amaral, 2015. "Choosing Expected Shortfall over VaR in Basel III Using Stochastic Dominance," Tinbergen Institute Discussion Papers 15-133/III, Tinbergen Institute.
    10. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.

    More about this item

    Keywords

    Median strategy; Value-at-Risk (VaR); daily capital charges; violation penalties; optimizing strategy; aggressive risk management; conservative risk management; Basel II Accord; VIX futures; global financial crisis (GFC).;

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kyo:wpaper:761. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ryo Okui). General contact details of provider: http://edirc.repec.org/data/iekyojp.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.