Continuous Record Asymptotics for Rolling Sample Variance Estimators

Dean Foster () and Daniel B. Nelson

No 163, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: It is widely known that conditional covariances of asset returns change over time. Researchers adopt many strategies to accommodate conditional heteroskedasticity. Among the most popular are: (a) chopping the data into short blocks of time and assuming homoskedasticity within the blocks, (b) performing one-sided rolling regressions, in which only data from, say, the preceding five year period is used to estimate the conditional covariance of returns at a given date, and (c) two-sided rolling regressions which use, say, five years of leads and five years of lags. GARCH amounts to a one-sided rolling regression with exponentially declining weights. We derive asymptotically optimal window lengths for standard rolling regressions and optimal weights for weighted rolling regressions. An empirical model of the S&P 500 stock index provides an example.

JEL-codes: C32 (search for similar items in EconPapers)
Date: 1994-08
Note: AP
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Citations: View citations in EconPapers (3)

Published as Foster, Dean P. and Dan B. Nelson. "Continuous Record Asymptotics For Rolling Sample Variance Estimators," Econometrica, 1996, v64(1), 139-174.

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Journal Article: Continuous Record Asymptotics for Rolling Sample Variance Estimators (1996)
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