Estimating Conditional Expectations When Volatility Fluctuates

40 Pages Posted: 25 May 2006 Last revised: 10 Jun 2007

See all articles by Robert F. Stambaugh

Robert F. Stambaugh

University of Pennsylvania - The Wharton School; National Bureau of Economic Research (NBER)

Date Written: August 1993

Abstract

Asymptotic variance of estimated parameters in models of conditional expectations are calculated analytically assuming a GARCH process for conditional volatility. Under such heteroskedasticity, OLS estimators or parameters in single-period models can posses substantially larger asymptotic variances the GMM estimators employing additional multiperiod moment conditions - an approach yielding no efficiency gain under homoskedasticity. In estimating models of long- horizon expectations, the VAR approach provides an efficiency advantage over long-horizon regressions under homoskedasticity, but that ordering can reverse under heteroskedasticity, especially when the conditional mean and variance are both persistent. In such cases, the VAR approach maintains a slight efficiency advantage if the OLS estimator is replaced by an alternative GMM estimator. Heteroskedasticity can increase dramatically the apparent asymptotic power advantages of long-horizon regressions to reject constant expectations against persistent alternatives.

Suggested Citation

Stambaugh, Robert F., Estimating Conditional Expectations When Volatility Fluctuates (August 1993). NBER Working Paper No. t0140. Available at SSRN: https://ssrn.com/abstract=225161

Robert F. Stambaugh (Contact Author)

University of Pennsylvania - The Wharton School ( email )

The Wharton School, Finance Department
University of Pennsylvania
Philadelphia, PA 19104-6367
United States
215-898-5734 (Phone)
215-898-6200 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
29
Abstract Views
911
PlumX Metrics