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Modeling the leverage effect with copulas and realized volatility

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Author Info

  • Ning, Cathy
  • Xu, Dinghai
  • Wirjanto, Tony S.

Abstract

In this paper, we propose the use of static and dynamic copulas to study the leverage effect in the S&P 500 index. Copula models can conveniently separate the leverage effect from the marginal distributions of the return and its volatility. Daily volatility is proxied by a measure of realized volatility, which is constructed from high-frequency data. We uncover a significant leverage effect in the S&P 500 index, and this leverage effect is found to be changing over time in a highly persistent manner. Moreover the dynamic copula models are shown to outperform the static counterparts.

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Bibliographic Info

Article provided by Elsevier in its journal Finance Research Letters.

Volume (Year): 5 (2008)
Issue (Month): 4 (December)
Pages: 221-227

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Handle: RePEc:eee:finlet:v:5:y:2008:i:4:p:221-227

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Web page: http://www.elsevier.com/locate/frl

Related research

Keywords: Leverage effect Copulas Tail dependence Realized volatility High frequency data;

References

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  1. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, 05.
  2. Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
  3. John Knight & Colin Lizieri & Stephen Satchell, 2005. "Diversification When It Hurts? The Joint Distributions of Real Estate and Equity Markets," Real Estate & Planning Working Papers rep-wp2005-16, Henley Business School, Reading University.
  4. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
  5. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
  6. Tim Bollerslev & Julia Litvinova & George Tauchen, 2006. "Leverage and Volatility Feedback Effects in High-Frequency Data," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(3), pages 353-384.
  7. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  8. Bollerslev, Tim & Zhou, Hao, 2006. "Volatility puzzles: a simple framework for gauging return-volatility regressions," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 123-150.
  9. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
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Cited by:
  1. Serra, Teresa & Gil, Jose Maria, 2012. "Biodiesel as a motor fuel price stabilization mechanism," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126056, International Association of Agricultural Economists.

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