Continuous Record Asymptotics for Rolling Sample Variance Estimators
Continuous Record Asymptotics for Rolling Sample Variance Estimators
复制标题
滚动样本方差估计器的连续记录渐近
DOI：
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发表时间：
1994
期刊：
影响因子：
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通讯作者：
Daniel B. Nelson
中科院分区：
文献类型：
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作者：
Dean Phillips Foster;Daniel B. Nelson
众所周知，资产回报的条件协方差会随着时间的推移而变化。研究人员采用许多策略来适应条件异方差性。其中最流行的是：（a）将数据分成较短的时间块并假设块内的同方差性，（b）执行单侧滚动回归，其中仅使用前五年期间的数据来估计给定日期回报的条件协方差，以及（c）双边滚动回归，例如使用五年的领先和五年的滞后。 GARCH 相当于权重呈指数下降的单边滚动回归。我们推导出标准滚动回归的渐近最佳窗口长度和加权滚动回归的最佳权重。标准普尔 500 股票指数的实证模型提供了一个例子。
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.