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分类依据与纠错

AI 根据所提供的论文材料归类:摘要研究资产价格高频离散观测下的积分协变、共同跳跃与波动率相关量。

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代码、数据与补充材料

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关注这篇论文的作者
Cecilia Mancini
Fabio Gobbi

离散观测下布朗协变与共同跳跃的分离识别

研究问题
当资产价格协变同时来自布朗运动与跳跃时,如何利用离散观测分别估计两部分,并处理不同步观测问题?
方法与思路
作者在可含无限活动跳跃的半鞅框架下使用阈值截断估计排除跳跃项,并在有限活动跳跃时结合观测筛选处理不同步数据。
材料陈述的贡献
摘要称可一致分离两类协变且无需规定波动率动态;有限活动跳跃下得到中心极限定理,并报告较小渐近方差与模拟表现。
阅读关注点
应核对阈值选择及跳跃活动条件,区分一致性与中心极限定理的适用范围,并检查不同步观测和微观结构噪声的处理。
阅读边界
仅据所给 Crossref 版本摘要,方差优势的比较条件未说明;低频模拟中噪声可忽略不等于证明对微观结构噪声稳健。

原文摘要

When the covariance between the risk factors of asset prices is due to both Brownian and jump components, the realized covariation (RC) approaches the sum of the integrated covariation (IC) with the sum of the co-jumps, as the observation frequency increases to infinity, in a finite and fixed time horizon. In this paper the two components are consistently separately estimated within a semimartingale framework with possibly infinite activity jumps. The threshold (or truncated) estimator $I\hat C_n $ is used, which substantially excludes from RC all terms containing jumps. Unlike in Jacod (2007, Universite de Paris-6) and Jacod (2008, Stochastic Processes and Their Applications 118, 517–559), no assumptions on the volatilities’ dynamics are required. In the presence of only finite activity jumps: 1) central limit theorems (CLTs) for $I\hat C_n $ and for further measures of dependence between the two Brownian parts are obtained; the estimation error asymptotic variance is shown to be smaller than for the alternative estimators of IC in the literature; 2) by also selecting the observations as in Hayashi and Yoshida (2005, Bernoulli 11, 359–379), robustness to nonsynchronous data is obtained. The proposed estimators are shown to have good finite sample performances in Monte Carlo simulations even with an observation frequency low enough to make microstructure noises’ impact on data negligible.

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