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Index-Option Pricing with Stochastic Volatility and the Value of Accurate Variance Forecasts

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  • Robert F. Engle
  • Alex Kane
  • Jaesun Noh

Abstract

In pricing primary-market options and in making secondary markets, financial intermediaries depend on the quality of forecasts of the variance of the underlying assets. Hence, the gain from improved pricing of options would be a measure of the value of a forecast of underlying asset returns. NYSE index returns over the period of 1968-1991 are used to suggest that pricing index options of up to 90-days maturity would be more accurate when: (1) using ARCH specifications in place of a moving average of squared returns; (2) using Hull and White's (1987) adjustment for stochastic variance in Black and Scholes's (1973) formula; (3) accounting explicitly for weekends and the slowdown of variance whenever the market is closed.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 4519.

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Date of creation: Nov 1993
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Publication status: published as Review of Derivatives Research, Volume 1, Number 2, 1996 , pp. 139-157
Handle: RePEc:nbr:nberwo:4519

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  1. Richardson, Matthew & Stock, James H., 1989. "Drawing inferences from statistics based on multiyear asset returns," Journal of Financial Economics, Elsevier, vol. 25(2), pages 323-348, December.
  2. Peter C.B. Phillips, 1985. "Time Series Regression with a Unit Root," Cowles Foundation Discussion Papers 740R, Cowles Foundation for Research in Economics, Yale University, revised Feb 1986.
  3. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
  4. 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.
  5. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
  6. Hull, John C & White, Alan D, 1987. " The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
  7. Matthew Richardson & James H. Stock, 1990. "Drawing Inferences From Statistics Based on Multi-Year Asset Returns," NBER Working Papers 3335, National Bureau of Economic Research, Inc.
  8. Brenner, Menachem & Galai, Dan, 1986. "Implied Interest Rates," The Journal of Business, University of Chicago Press, vol. 59(3), pages 493-507, July.
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Citations

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Cited by:
  1. Ayla Ogus, 2002. "Pricing of S&P 100 Index Options Based On Garch Volatility Estimates," Working Papers 0201, Izmir University of Economics.
  2. Lopez, Jose A, 2001. "Evaluating the Predictive Accuracy of Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 87-109, March.
  3. Eric Jacquier & Robert Jarrow, . "Model Error in Contingent Claim Models (Dynamic Evaluation)," Rodney L. White Center for Financial Research Working Papers 07-96, Wharton School Rodney L. White Center for Financial Research.
  4. Michel LUBRANO, 2001. "Smooth Transition Garch Models : a Baysian Perspective," Discussion Papers (REL - Recherches Economiques de Louvain) 2001032, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
  5. Schmitt, Christian & Kaehler, Jürgen, 1996. "Delta-neutral volatility trading with intra-day prices: an application to options on the DAX," ZEW Discussion Papers 96-25, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  6. Michael S. Gibson & Brian H. Boyer, 1997. "Evaluating forecasts of correlation using option pricing," International Finance Discussion Papers 600, Board of Governors of the Federal Reserve System (U.S.).
  7. Catalin Starica & Stefano Herzel & Tomas Nord, 2005. "Why does the GARCH(1,1) model fail to provide sensible longer- horizon volatility forecasts?," Econometrics 0508003, EconWPA.
  8. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
  9. David Bates & Roger Craine, 1998. "Valuing the Futures Market Clearinghouse's Default Exposure During the 1987 Crash," NBER Working Papers 6505, National Bureau of Economic Research, Inc.
  10. Claessen, Holger & Mittnik, Stefan, 2002. "Forecasting stock market volatility and the informational efficiency of the DAX-index options market," CFS Working Paper Series 2002/04, Center for Financial Studies (CFS).
  11. Jaesun Noh & Robert F. Engle & Alex Kane, 1993. "A Test of Efficiency for the S&P Index Option Market Using Variance Forecasts," NBER Working Papers 4520, National Bureau of Economic Research, Inc.
  12. Jose A. Lopez & Christian A. Walter, 2000. "Evaluating covariance matrix forecasts in a value-at-risk framework," Working Paper Series 2000-21, Federal Reserve Bank of San Francisco.
  13. James Chong, 2004. "Options trading profits from correlation forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 14(15), pages 1075-1085.

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