Valuation of Variance Forecast with Simulated Option Markets

40 Pages Posted: 30 Aug 2010 Last revised: 23 Oct 2010

See all articles by Robert F. Engle

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)

Che-Hsiung Hong

affiliation not provided to SSRN

Alex Kane

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

Date Written: May 1990

Abstract

An appropriate metric for the success of an algorithm to forecast the variance of the rate of return on a capital asset could be the incremental profit from substituting it for the next best alternative. We propose a framework to assess incremental profits for competing algorithms to forecast the variance of a prespecified asset. The test is based on the return history of the asset in question. A hypothetical insurance market is set up, where competing forecasting algorithms are used. One algorithm is used by each hypothetical agent in an "ex post ante" forecasting exercise, using the available history of the asset returns. The profit differentials across agents (in various groupings) reflect incremental values of the forecasting algorithms.The technique is demonstrated with the NYSE portfolio, over the period of July 22, 1966 to December 31, 1985. For the limited set of alternative specifications, we find that GARCH(1,1) yields better profits than the 3 competing specifications. The profit from pricing one-day options on the NYSE portfolio significant. The evidence also suggests that using a limited estimation period may be preferable to estimating specification parameters from all available observations. Finally, the hedging activity that requires a variance determined hedge ratio is an important component of the success of a variance forecast-algorithm.

Suggested Citation

Engle, Robert F. and Hong, Che-Hsiung and Kane, Alex, Valuation of Variance Forecast with Simulated Option Markets (May 1990). NBER Working Paper No. w3350. Available at SSRN: https://ssrn.com/abstract=1667803

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

Che-Hsiung Hong

affiliation not provided to SSRN

No Address Available

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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