The simplest and most natural vol-of-vol estimator, the pre-estimated spot variance-based realized variance, is typically plagued by a large finite-sample bias. In this paper, we analytically show that allowing for the overlap of consecutive local windows to pre-estimate the spot variance may correct for this bias. In particular, we provide a feasible rule for the bias-optimal selection of the length of local windows when the volatility is a CKLS process. The effectiveness of this rule for practical applications is supported by numerical and empirical analyses.

中文翻译：

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偏差最优 vol-of-vol 估计：窗口重叠的作用

最简单和最自然的 vol-of-vol 估计器，即基于预估计点方差的已实现方差，通常受到大的有限样本偏差的困扰。在本文中，我们分析表明，允许连续局部窗口的重叠来预估计点方差可以纠正这种偏差。特别是，当波动率是 CKLS 过程时，我们为局部窗口长度的偏差最优选择提供了一个可行的规则。该规则在实际应用中的有效性得到了数值和经验分析的支持。