Local SIML estimation of some Brownian and jump functionals under market micro-structure noise
Local SIML estimation of some Brownian and jump functionals under market micro-structure noise
复制标题
市场微观结构噪声下部分布朗泛函和跳跃泛函的局部 SIML 估计
DOI：
10.1007/s42081-022-00172-0
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Naoto Kunitomo and Seisho Sato
中科院分区：
文献类型：
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作者：
Tadahiro Nakajima;Shigeyuki Hamori;加藤 健;国友直人;Naoto Kunitomo and Seisho Sato
这篇论文是对《数据科学：现在和未来》特刊的一篇贡献，因为这一主题一直是并将是当代数据科学的一个活跃领域。到目前为止，高频金融数据已经普遍存在。为了从市场微结构噪声下的高频金融数据中估计布朗泛函和跳跃泛函，我们引入了一种新的局部估计方法来估计Ito半鞅过程的积分波动率和高阶变差。虽然将已实现波动率(RV)估计推广到没有微观市场噪声的一般扩散-跳跃过程是简单的，但在存在微观市场噪声的情况下估计布朗泛函和跳跃泛函可能并不容易。在这项研究中，我们发展了局部SIML(LSIML)方法，它是由ukitomo等人提出的分离信息最大似然(SIML)方法的扩展。(高频金融数据的分离信息最大似然方法，2018)和库尼托莫和库里苏(日本统计数据科学(JJSD)4(1)：601-641,2021)。新的LSIML方法简单，LSIML估计量具有理想的渐近性质和合理的有限样本性质。
This paper is a contribution to a special issue onData Science: Present and Future, because the main topic has been and will be in an active area of contemporary data science. High-frequency financial data are commonly available by now. To estimate Brownian and jump functionals from high-frequency financial data under market micro-structure noise, we introduce a new local estimation method of the integrated volatility and higher order variation of Ito’s semi-martingale processes. Although extending the realized volatility (RV) estimation to the general diffusion-jump processes without micro-market noise is straightforward, estimating Brownian and jump functionals in the presence of micro-market noise may not be easy. In this study, we develop the local SIML (LSIML) method, which is an extension of the separating information maximum likelihood (SIML) method proposed by Kunitomo et al. (Separating information maximum likelihood method for high-frequency financial data, 2018) and Kunitomo and Kurisu (Jpn J Stat Data Sci (JJSD) 4(1):601–641, 2021). The new LSIML method is simple, and the LSIML estimator has some desirable asymptotic properties and reasonable finite sample properties.