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Estimating Financial Risk Measures for Futures Positions:A Non-Parametric Approach

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  • John Cotter

    (University College Dublin, Ireland)

  • Kevin Dowd

    (The University of Nottingham)

Abstract

This paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned on the confidence level. Our findings indicate that all risk measures increase dramatically and their estimators deteriorate in precision when their respective conditioning parameter increases. Results also suggest that estimates of spectral risk measures and their precision levels are of comparable orders of magnitude as those of more conventional risk measures. Running head: financial risk measures for futures positions.

Suggested Citation

  • John Cotter & Kevin Dowd, 2011. "Estimating Financial Risk Measures for Futures Positions:A Non-Parametric Approach," Working Papers 200613, Geary Institute, University College Dublin.
    • Handle: RePEc:ucd:wpaper:2006/13
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    References listed on IDEAS

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    1. Brooks, C. & Clare, A.D. & Dalle Molle, J.W. & Persand, G., 2005. "A comparison of extreme value theory approaches for determining value at risk," Journal of Empirical Finance, Elsevier, vol. 12(2), pages 339-352, March.
    2. Acerbi, Carlo, 2002. "Spectral measures of risk: A coherent representation of subjective risk aversion," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1505-1518, July.
    3. Upper, Christian & Werner, Thomas, 2002. "Time Variation in the Tail Behaviour of Bund Futures Returns," Discussion Paper Series 1: Economic Studies 2002,25, Deutsche Bundesbank.
    4. Thomas Werner & Christian Upper, 2004. "Time variation in the tail behavior of Bund future returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(4), pages 387-398, April.
    5. Matthew Pritsker, 1997. "Evaluating Value at Risk Methodologies: Accuracy versus Computational Time," Journal of Financial Services Research, Springer;Western Finance Association, vol. 12(2), pages 201-242, October.
    6. Cotter, John & Dowd, Kevin, 2006. "Extreme spectral risk measures: An application to futures clearinghouse margin requirements," Journal of Banking & Finance, Elsevier, vol. 30(12), pages 3469-3485, December.
    7. Christian Gourieroux & Wei Liu, 2006. "Sensitivity Analysis of Distortion Risk Measures," Working Papers 2006-33, Center for Research in Economics and Statistics.
    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. John Cotter, 2004. "Minimum capital requirement calculations for UK futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(2), pages 193-220, February.
    10. Broussard, John Paul, 2001. "Extreme-value and margin setting with and without price limits," The Quarterly Review of Economics and Finance, Elsevier, vol. 41(3), pages 365-385.
    11. Song Xi Chen, 2005. "Nonparametric Inference of Value-at-Risk for Dependent Financial Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(2), pages 227-255.
    12. Hsieh, David A., 1993. "Implications of Nonlinear Dynamics for Financial Risk Management," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(1), pages 41-64, March.
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    Cited by:

    1. Lima Miquelluti, Daniel & Ozaki, Vitor & Miquelluti, David J., 2020. "An application of geographically weighted quantile LASSO to weather index insurance design," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304288, Agricultural and Applied Economics Association.
    2. Mitra, Sovan, 2017. "Efficient option risk measurement with reduced model risk," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 163-174.
    3. Wächter, Hans Peter & Mazzoni, Thomas, 2013. "Consistent modeling of risk averse behavior with spectral risk measures," European Journal of Operational Research, Elsevier, vol. 229(2), pages 487-495.
    4. Mario Brandtner, 2016. "Spektrale Risikomaße: Konzeption, betriebswirtschaftliche Anwendungen und Fallstricke," Management Review Quarterly, Springer, vol. 66(2), pages 75-115, April.
    5. Brandtner, Mario & Kürsten, Wolfgang, 2014. "Decision making with Conditional Value-at-Risk and spectral risk measures: The problem of comparative risk aversion," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100615, Verein für Socialpolitik / German Economic Association.
    6. Mario Brandtner, 2016. "“Spectral Risk Measures: Properties and Limitations”: Comment on Dowd, Cotter, and Sorwar," Journal of Financial Services Research, Springer;Western Finance Association, vol. 49(1), pages 121-131, February.
    7. Mozumder, Sharif & Choudhry, Taufiq & Dempsey, Michael, 2018. "Spectral measures of risk for international futures markets: A comparison of extreme value and Lévy models," Global Finance Journal, Elsevier, vol. 37(C), pages 248-261.
    8. Brandtner, Mario & Kürsten, Wolfgang, 2015. "Decision making with Expected Shortfall and spectral risk measures: The problem of comparative risk aversion," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 268-280.

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    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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