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Optimal Nonparametric Methods for Ito Processes Based on High-Frequency Data

基于高频数据的 Ito 过程的最优非参数方法

基本信息

批准号：
2015323
负责人：
Jose Figueroa-Lopez
金额：
$ 15万
依托单位：
Washington University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015323&HistoricalAwards=false
关键词：
Optimal Nonparametric Methods Ito Processes

项目摘要

The project will develop new statistical methods to analyze stochastic phenomena that inherently evolves in time. The methods will be based on high-frequency monitoring of the phenomena under study, which is of widening use in many fields such as finance, meteorology, biology, neuroscience, turbulence, statistical physics, seismology, and telecommunication. Thus, the research will foster more interaction between applied scientists and statisticians. The research and educational elements of the project will also serve as a training tool for both undergraduate and graduate students by forming synergistic research groups involving all levels. Nonparametric methods are powerful statistical tools to reduce the model misspecification error, and high-frequency-based statistical analysis is a natural route to take when estimating the fine statistical features of continuous-time stochastic processes. Though the literature combining these two approaches has grown significantly during the last two decades, comparatively little work has been done to analyze and correct the sensitivity of methods to tuning parameters. The project will address these needs by (i) developing a unified approach for optimal kernel estimation of the spot volatility of an Ito process in the presence of leverage and microstructure noise; (ii) devising of optimal jump detection and integrated variance estimation methods, under the presence of stochastic volatility and infinite jump activity, via thresholding or shrinkage of the process' increments or wavelet coefficients; (iii) establishing theoretical guarantees for the data-driven plugging implementation methods resulting from the optimal schemes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将开发新的统计方法来分析随时间内在演变的随机现象。这些方法将以所研究现象的高频监测为基础，在金融、气象学、生物学、神经科学、湍流、统计物理学、地震学和电信等许多领域都有广泛的用途。因此，这项研究将促进应用科学家和统计学家之间更多的互动。该项目的研究和教育部分还将通过组成涉及各级的协同研究小组，作为本科生和研究生的培训工具。非参数方法是减少模型误设误差的强大统计工具，而基于高频的统计分析是估计连续时间随机过程的精细统计特征时的自然途径。虽然这两种方法相结合的文献在过去的二十年中显着增长，相对较少的工作已经做了分析和校正的方法调整参数的灵敏度。该项目将通过以下方式满足这些需求：（i）在存在杠杆和微观结构噪声的情况下，开发Ito过程的现货波动率的最佳核估计的统一方法;（ii）在存在随机波动率和无限跳跃活动的情况下，通过对过程的增量或小波系数进行阈值化或收缩，设计最佳跳跃检测和综合方差估计方法;（iii）为最佳方案产生的数据驱动封堵实施方法建立理论保证。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。