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Modified Estimators of the Expected Shortfall

Author

Listed:
  • Deepak Jadhav

    (Deepak Jadhav is at the Department of Statistics, University of Pune, Maharashtra, India. e-mail: jadhavdeepak1@gmail.com)

  • T.V. Ramanathan

    (T.V. Ramanathan is at the Department of Statistics, University of Pune, Maharashtra, India. e-mail: ram@stats.unipune.ernet.in)

  • U.V. Naik-Nimbalkar

    (U.V. Naik-Nimbalkar is at the Department of Statistics, University of Pune, Maharashtra, India. e-mail: uvnaik@stats.unipune.ernet.in)

Abstract

The coherent risk measure Expected Shortfall is popularly considered as an alternative to Value-at-Risk. We briefly review all existing parametric and non-parametric methods to estimate Expected Shortfall. The historical method is considered as the best method of estimation for the Expected Shortfall, though it has a serious disadvantage of over-estimation in the presence of outliers in the return data. In this article, we propose two non-parametric estimators of Expected Shortfall which are robust to outliers. We estimate the Expected Shortfall corresponding to daily returns of some of the selected assets and indices of the Indian (BSE and NSE) and foreign stock markets (NYSE and LSE). The backtesting procedure boasts in confirming that the proposed non-parametric estimators are the best alternatives to the historical method in avoiding over-estimation of Expected Shortfall.

Suggested Citation

  • Deepak Jadhav & T.V. Ramanathan & U.V. Naik-Nimbalkar, 2009. "Modified Estimators of the Expected Shortfall," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 8(2), pages 87-107, May.
    • Handle: RePEc:sae:emffin:v:8:y:2009:i:2:p:87-107
    DOI: 10.1177/097265270900800201
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    References listed on IDEAS

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    1. Yamai, Yasuhiro & Yoshiba, Toshinao, 2002. "Comparative Analyses of Expected Shortfall and Value-at-Risk (2): Expected Utility Maximization and Tail Risk," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 20(2), pages 95-115, April.
    2. Carlo Acerbi & Dirk Tasche, 2002. "Expected Shortfall: A Natural Coherent Alternative to Value at Risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 31(2), pages 379-388, July.
    3. Tasche, Dirk, 2002. "Expected shortfall and beyond," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1519-1533, July.
    4. Fermanian, Jean-David & Scaillet, Olivier, 2005. "Sensitivity analysis of VaR and Expected Shortfall for portfolios under netting agreements," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 927-958, April.
    5. Fotios C. Harmantzis & Linyan Miao & Yifan Chien, 2006. "Empirical study of value-at-risk and expected shortfall models with heavy tails," Journal of Risk Finance, Emerald Group Publishing, vol. 7(2), pages 117-135, March.
    6. O. Scaillet, 2004. "Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall," Mathematical Finance, Wiley Blackwell, vol. 14(1), pages 115-129, January.
    7. 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.
    8. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
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    Cited by:

    1. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.

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    More about this item

    Keywords

    Value-at-risk; expected shortfall; historical method; backtesting; bootstrap; JEL Classification: G11; JEL Classification: G12;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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