Predictable dynamics in the S&P 500 index options implied volatility surface
One key stylized fact in the empirical option pricing literature is the existence of an implied volatility surface (IVS). The usual approach consists of fitting a linear model linking the implied volatility to the time to maturity and the moneyness, for each cross section of options data. However, recent empirical evidence suggests that the parameters characterizing the IVS change over time. In this paper we study whether the resulting predictability patterns in the IVS coefficients may be exploited in practice. We propose a two-stage approach to modeling and forecasting the S&P 500 index options IVS. In the first stage we model the surface along the cross-sectional moneyness and time-to-maturity dimensions, similarly to Dumas et al. (1998). In the second-stage we model the dynamics of the cross-sectional first-stage implied volatility surface coefficients by means of vector autoregression models. We find that not only the S&P 500 implied volatility surface can be successfully modeled, but also that its movements over time are highly predictable in a statistical sense. We then examine the economic significance of this statistical predictability with mixed findings. Whereas profitable delta-hedged positions can be set up that exploit the dynamics captured by the model under moderate transaction costs and when trading rules are selective in terms of expected gains from the trades, most of this profitability disappears when we increase the level of transaction costs and trade multiple contracts off wide segments of the IVS. This suggests that predictability of the time-varying S&P 500 implied volatility surface may be not inconsistent with market efficiency.
|Date of creation:||2005|
|Date of revision:|
|Publication status:||Published in Journal of Business, May 2006, 79(3), pp. 1591-1635|
|Contact details of provider:|| Postal: P.O. Box 442, St. Louis, MO 63166|
Web page: http://www.stlouisfed.org/
More information through EDIRC
|Order Information:|| Email: |
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Garcia, Rene & Luger, Richard & Renault, Eric, 2003.
"Empirical assessment of an intertemporal option pricing model with latent variables,"
Journal of Econometrics,
Elsevier, vol. 116(1-2), pages 49-83.
- GARCIA,René & LUGER, Richard & RENAULT, Éric, 2001. "Empirical Assessment of an Intertemporal Option Pricing Model with Latent variables," Cahiers de recherche 2001-10, Universite de Montreal, Departement de sciences economiques.
- René Garcia & Richard Luger & Eric Renault, 2000. "Empirical Assessment of an Intertemporal Option Pricing Model with Latent Variables," Working Papers 2000-56, Centre de Recherche en Economie et Statistique.
- Garcia, R. & Luger, R. & Renault, E., 2001. "Empirical Assessment of an Intertemporal option Pricing Model with Latent variables," Cahiers de recherche 2001-10, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-81.
- Bakshi, Gurdip S. & Zhiwu, Chen, 1997.
"An alternative valuation model for contingent claims,"
Journal of Financial Economics,
Elsevier, vol. 44(1), pages 123-165, April.
- Gurdip S. Bakshi & Zhiwu Chen, 1996. "An Alternative Valuation Model for Contingent Claims," Yale School of Management Working Papers ysm78, Yale School of Management.
- Diebold, Francis X. & Li, Canlin, 2006.
"Forecasting the term structure of government bond yields,"
Journal of Econometrics,
Elsevier, vol. 130(2), pages 337-364, February.
- Diebold, Francis X. & Li, Canlin, 2003. "Forecasting the term structure of government bond yields," CFS Working Paper Series 2004/09, Center for Financial Studies (CFS).
- Francis X. Diebold & Canlin Li, 2003. "Forecasting the Term Structure of Government Bond Yields," NBER Working Papers 10048, National Bureau of Economic Research, Inc.
- Francis X. Diebold & Canlin Li, 2002. "Forecasting the Term Structure of Government Bond Yields," Center for Financial Institutions Working Papers 02-34, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Christoffersen, Peter & Jacobs, Kris, 2004.
"The importance of the loss function in option valuation,"
Journal of Financial Economics,
Elsevier, vol. 72(2), pages 291-318, May.
- Peter Christoffersen & Kris Jacobs, 2003. "The Importance of the Loss Function in Option Valuation," CIRANO Working Papers 2003s-52, CIRANO.
- Day, Theodore E. & Lewis, Craig M., 1988. "The behavior of the volatility implicit in the prices of stock index options," Journal of Financial Economics, Elsevier, vol. 22(1), pages 103-122, October.
- René Garcia & Eric Ghysels & Éric Renault, 2004. "The Econometrics of Option Pricing," CIRANO Working Papers 2004s-04, CIRANO.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Engle, Robert F & Ng, Victor K, 1993.
" Measuring and Testing the Impact of News on Volatility,"
Journal of Finance,
American Finance Association, vol. 48(5), pages 1749-78, December.
- Robert F. Engle & Victor K. Ng, 1991. "Measuring and Testing the Impact of News on Volatility," NBER Working Papers 3681, National Bureau of Economic Research, Inc.
- Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
- Allan Timmermann & Massimo Guidolin, 2001.
"Option Prices under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities,"
FMG Discussion Papers
dp397, Financial Markets Group.
- Guidolin, Massimo & Timmermann, Allan, 2003. "Option prices under Bayesian learning: implied volatility dynamics and predictive densities," Journal of Economic Dynamics and Control, Elsevier, vol. 27(5), pages 717-769, March.
- Guidolin, Massimo & Timmermann, Allan G, 2001. "Option Prices under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities," CEPR Discussion Papers 3005, C.E.P.R. Discussion Papers.
- Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-54, May-June.
When requesting a correction, please mention this item's handle: RePEc:fip:fedlwp:2005-010. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anna Xiao)
If references are entirely missing, you can add them using this form.