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

31 Pages Posted: 9 Jul 2000

See all articles by Jaesun Noh

Jaesun Noh

Korea Advanced Institute of Science and Technology (KAIST) - Graduate School of Finance

Robert F. Engle

New York University - Leonard N. Stern School of Business - Department of Economics; New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER)

Alex Kane

University of California, San Diego (UCSD) - Graduate School of International Relations and Pacific Studies (IRPS)

Date Written: November 1993

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

Noh, Jaesun and Engle, Robert F. and Kane, Alex, A Test of Efficiency for the S&P Index Option Market Using Variance Forecasts (November 1993). NBER Working Paper No. w4520. Available at SSRN: https://ssrn.com/abstract=226762

Jaesun Noh

Korea Advanced Institute of Science and Technology (KAIST) - Graduate School of Finance ( email )

207-43 Cheongryangri-2dong 130-722
Seoul
Korea

Robert F. Engle (Contact Author)

New York University - Leonard N. Stern School of Business - Department of Economics ( email )

269 Mercer Street
New York, NY 10003
United States

New York University (NYU) - Department of Finance

Stern School of Business
44 West 4th Street
New York, NY 10012-1126
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Alex Kane

University of California, San Diego (UCSD) - Graduate School of International Relations and Pacific Studies (IRPS) ( email )

9500 Gilman Drive
La Jolla, CA 92093-0519
United States

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