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VaR and expected shortfall: a non-normal regime switching framework

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  • Robert Elliott
  • Hong Miao

Abstract

We have developed a regime switching framework to compute the Value at Risk and Expected Shortfall measures. Although Value at Risk as a risk measure has been criticized by some researchers for lack of subadditivity, it is still a central tool in banking regulations and internal risk management in the finance industry. In contrast, Expected Shortfall is coherent and convex, so it is a better measure of risk than Value at Risk. Expected Shortfall is widely used in the insurance industry and has the potential to replace Value at Risk as a standard risk measure in the near future. We have proposed regime switching models to measure value at risk and expected shortfall for a single financial asset as well as financial portfolios. Our models capture the volatility clustering phenomenon and variance-independent variation in the higher moments by assuming the returns follow Student-t distributions.

Suggested Citation

  • Robert Elliott & Hong Miao, 2009. "VaR and expected shortfall: a non-normal regime switching framework," Quantitative Finance, Taylor & Francis Journals, vol. 9(6), pages 747-755.
    • Handle: RePEc:taf:quantf:v:9:y:2009:i:6:p:747-755
    DOI: 10.1080/14697680902849320
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    References listed on IDEAS

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    1. Chu-Hsiung Lin & Shan-Shan Shen, 2006. "Can the student-t distribution provide accurate value at risk?," Journal of Risk Finance, Emerald Group Publishing, vol. 7(3), pages 292-300, May.
    2. Billio, Monica & Pelizzon, Loriana, 2000. "Value-at-Risk: a multivariate switching regime approach," Journal of Empirical Finance, Elsevier, vol. 7(5), pages 531-554, December.
    3. Eugene F. Fama, 1963. "Mandelbrot and the Stable Paretian Hypothesis," The Journal of Business, University of Chicago Press, vol. 36, pages 420-420.
    4. Ryohei Kawata & Masaaki Kijima, 2007. "Value-at-risk in a market subject to regime switching," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 609-619.
    5. Tasche, Dirk, 2002. "Expected shortfall and beyond," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1519-1533, July.
    6. Elliott, Robert J. & Hunter, William C. & Jamieson, Barbara M., 1998. "Drift and volatility estimation in discrete time," Journal of Economic Dynamics and Control, Elsevier, vol. 22(2), pages 209-218, February.
    7. Bormetti, Giacomo & Cisana, Enrica & Montagna, Guido & Nicrosini, Oreste, 2007. "A non-Gaussian approach to risk measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 532-542.
    8. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    9. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Cited by:

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    2. Jiliang Sheng & Juchao Li & Jun Yang, 2022. "Tail Dependency and Risk Spillover between Oil Market and Chinese Sectoral Stock Markets—An Assessment of the 2013 Refined Oil Pricing Reform," Energies, MDPI, vol. 15(16), pages 1-19, August.

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