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Unbiased weighted variance and skewness estimators for overlapping returns

机译:收益重叠的无偏加权方差和偏度估计量

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摘要

This article develops unbiased weighted variance and skewness estimators for overlapping return distributions. These estimators extend the variance estimation methods constructed in Bod et. al. (Applied Financial Economics 12:155-158, 2002) and Lo and MacKinlay (Review of Financial Studies 1:41-66, 1988). In addition, they may be used in overlapping return variance or skewness ratio tests as in Charles and Darné (Journal of Economic Surveys 3:503-527, 2009) and Wong (Cardiff Economics Working Papers, 2016). An example using synthetic overlapping returns from a model fit to data from the SPY S&P 500 exchange traded fund is given in order to demonstrate under which circumstances the unbiased correction becomes significant in skewness estimation. Finally, we compare the effect of the HAC weighting schemes of Andrews (Econometrica 53:817-858, 1991) as a function of sample size and overlapping return window length.
机译:本文为重叠的收益分布开发了无偏加权方差和偏度估计量。这些估计器扩展了Bod等人构建的方差估计方法。等(Applied Financial Economics 12:155-158,2002)和Lo and MacKinlay(《金融研究评论》 1:41-66,1988)。此外,它们可用于重叠的回报方差或偏度比检验,如Charles和Darné(经济调查杂志3:503-527,2009)和Wong(卡迪夫经济学工作论文,2016)中所述。给出了一个使用来自模型的合成重叠收益拟合来自SPY S&P 500交易所买卖基金数据的示例,目的是证明在哪种情况下,偏度估计中无偏校正变得显着。最后,我们比较了安德鲁斯的HAC加权方案的效果(Econometrica 53:817-858,1991),它是样本大小和重叠返回窗口长度的函数。

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