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Continuous Record Asymptotics for Rolling Sample Variance Estimators

Listed author(s):
  • Dean P. Foster
  • Daniel B. Nelson

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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0163.

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Date of creation: Aug 1994
Publication status: published as Foster, Dean P. and Dan B. Nelson. "Continuous Record Asymptotics For Rolling Sample Variance Estimators," Econometrica, 1996, v64(1), 139-174.
Handle: RePEc:nbr:nberte:0163
Note: AP
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