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Forecasting Volatility Using Long Memory and Comovements: An Application to Option Valuation under SFAS 123R

Published online by Cambridge University Press:  19 February 2010

George J. Jiang  and
Yisong S. Tian
George J. Jiang
Affiliation:
Eller College of Management, University of Arizona, PO Box 210108, Tucson, AZ 85721. gjiang@eller.arizona.edu
Yisong S. Tian
Affiliation:
Schulich School of Business, York University, 4700 Keele St., Toronto, ON, M3J 1P3, Canada. ytian@ssb.yorku.ca
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Abstract

Horizon-matched historical volatility is commonly used to forecast future volatility for option valuation under the Statement of Financial Accounting Standards (SFAS) 123R. In this paper, we empirically investigate the performance of using historical volatility to forecast long-term stock return volatility in comparison with a number of alternative forecasting methods. In analyzing forecasting errors and their impact on reported income due to option expensing, we find that historical volatility is a poor forecast for long-term volatility and that shrinkage adjustment toward comparable-firm volatility only slightly improves its performance. Forecasting performance can be improved substantially by incorporating both long memory and comovements with common market factors. We also experiment with a simple mixed-horizon realized volatility model and find its long-term forecasting performance to be more accurate than historical forecasts but less accurate than long-memory forecasts.

Type
Research Articles
Information
Journal of Financial and Quantitative Analysis , Volume 45 , Issue 2 , April 2010 , pp. 503 - 533
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2010

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