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A Test of Efficiency for the S&P Index Option Market Using Variance Forecasts

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  • Jaesun Noh
  • Robert F. Engle
  • Alex Kane

Abstract

To forecast future option prices, autoregressive models of implied volatility derived from observed option prices are commonly employed [see Day and Lewis (1990), and Harvey and Whaley (1992)]. In contrast, the ARCH model proposed by Engle (1982) models the dynamic behavior in volatility, forecasting future volatility using only the return series of an asset. We assess the performance of these two volatility prediction models from S&P 500 index options market data over the period from September 1986 to December 1991 by employing two agents who trade straddles, each using one of the two different methods of forecast. Straddle trading is employed since a straddle does not need to be hedged. Each agent prices options according to her chosen method of forecast, buying (selling) straddles when her forecast price for tomorrow is higher (lower) than today's market closing price, and at the end of each day the rates of return are computed. We find that the agent using the GARCH forecast method earns greater profit than the agent who uses the implied volatility regression (IVR) forecast model. In particular, the agent using the GARCH forecast method earns a profit in excess of a cost of $0.25 per straddle with the near-the-money straddle trading.

Suggested Citation

  • 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.
    • Handle: RePEc:nbr:nberwo:4520
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    References listed on IDEAS

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    Cited by:

    1. Ahmad M. Talafha & Emmanuel Thompson, 2017. "On Valuing European Option: VAR-COVAR Approach," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 6(3), pages 1-1.
    2. Rafiqul Bhuyan, 2002. "Information, Alternative Markets, and Security Price Processes: A Survey of Literature," Finance 0211002, University Library of Munich, Germany.
    3. Eduardo Rossi, 2010. "Univariate GARCH models: a survey (in Russian)," Quantile, Quantile, issue 8, pages 1-67, July.
    4. Brock Johnson & Jonathan Batten, 2003. "Forecasting Credit Spread Volatility: Evidence from the Japanese Eurobond Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 10(4), pages 335-357, December.
    5. Stanislav Anatolyev & Nikolay Gospodinov, 2012. "Asymptotics of near unit roots (in Russian)," Quantile, Quantile, issue 10, pages 57-71, December.
    6. Stanislav Anatolyev, 2007. "The basics of bootstrapping (in Russian)," Quantile, Quantile, issue 3, pages 1-12, September.
    7. Marc Saez, 1997. "Option pricing under stochastic volatility and stochastic interest rate in the Spanish case," Applied Financial Economics, Taylor & Francis Journals, vol. 7(4), pages 379-394.

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