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

Author

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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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    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. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
    4. 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.
    5. Mario Brandtner, 2016. "Spektrale Risikomaße: Konzeption, betriebswirtschaftliche Anwendungen und Fallstricke," Management Review Quarterly, Springer, vol. 66(2), pages 75-115, April.
    6. 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.
    7. Daniel Lima Miquelluti & Vitor Augusto Ozaki & David José Miquelluti, 2022. "An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 26(3), pages 200387-2003.
    8. 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.
    9. 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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