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英文文献：Bootstrapping realized volatility and realized beta under a local Gaussianity assumption-在局部高斯性假设下Bootstrapping已实现的波动率和已实现的beta
英文文献作者：Ulrich Hounyo
英文文献摘要：
The main contribution of this paper is to propose a new bootstrap method for statistics based on high frequency returns. The new method exploits the local Gaussianity and the local constancy of volatility of high frequency returns, two assumptions that can simplify inference in the high frequency context, as recently explained by Mykland and Zhang (2009). Our main contributions are as follows. First, we show that the local Gaussian bootstrap is firstorder consistent when used to estimate the distributions of realized volatility and ealized betas. Second, we show that the local Gaussian bootstrap matches accurately the first four cumulants of realized volatility, implying that this method provides third-order refinements. This is in contrast with the wild bootstrap of Gon?alves and Meddahi (2009), which is only second-order correct. Third, we show that the local Gaussian bootstrap is able to provide second-order refinements for the realized beta, which is also an improvement of the existing bootstrap results in Dovonon, Gon?alves and Meddahi (2013) (where the pairs bootstrap was shown not to be second-order correct under general stochastic volatility). Lastly, we provide Monte Carlo simulations and use empirical data to compare the finite sample accuracy of our new bootstrap confidence intervals for integrated volatility and integrated beta with the existing results.
本文的主要贡献是提出了一种新的基于高频收益的统计bootstrap方法。新方法利用高频收益波动的局部高斯性和局部恒常性，这两个假设可以简化高频背景下的推断，最近由Mykland和Zhang(2009)解释。我们的主要贡献如下。首先，我们证明了局部高斯自举法在估计已实现波动率和已化贝塔分布时是一阶一致的。其次，我们证明了局部高斯自举匹配准确的前四个已实现的波动性累积量，这意味着该方法提供了三阶的细化。这与Goncalves和Meddahi(2009)的疯狂自举形成对比，后者只是二阶正确。第三，我们证明了局部高斯bootstrap能够为已实现的beta提供二阶改进，这也是对Dovonon、Goncalves和Meddahi(2013)中现有bootstrap结果的改进(在一般随机波动率下，bootstrap对被证明不是二阶正确)。最后，我们提供蒙特卡洛模拟和使用经验数据比较有限样本精度的新bootstrap置信区间的综合波动和综合beta与现有的结果。
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