关于金融高频数据的统计推断

项目名称： 关于金融高频数据的统计推断

项目编号： No.11301236

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 数理科学和化学

项目作者： 李翠霞

作者单位： 兰州大学

项目金额： 22万元

中文摘要： 随着社会经济的急速发展，关于金融高频数据中涉及到的各项指标的研究已引起了人们广泛的关注，尤其对股票、期货及其衍生物在组合管理、风险分析等方面的分析更是受到人们的高度重视。本项目拟在我们近几年研究的基础上，进一步从伊藤过程与分数布朗运动的特性出发，结合高频数据对资产价格的潜在过程进行研究。其研究意义在于，理论上，我们通过对资产价格中关注的一些指标进行估计，同时对资产价格的驱动过程进行一系列的检验，可以进一步丰富高频数据、金融统计与极限理论等方面的理论知识；应用上，所得的结果在资产价格、电信与环境的真实数据分析中也有指导性的作用。

中文关键词： 高频数据；自权重积分交叉波动率；半鞅模型；内生性；高维数据

英文摘要： In today's world, many fields are confronted with increasingly large amounts of data. Financial data sampled with high frequency is no exception. These staggering amounts of data pose special challenges to the world of finance, as traditional models and information technology tools can be poorly suited to grapple with their size and complexity. Probabilitstic modeling and statistical data analysis attempt to discover order from apparent disorder. It is well known that the high frequency data have some unique characteristics that do not appear in lower frequencies, such as jumps, microstructure noise non-synchronous trading. Thus, analysis of these data introduces many new challenges to financial economists and statisticians since the classical theory on stochastic processes and statistics is no longer enough to solve the mentioned problems. The purpose of this project is to investigate some of the issues in the study of high frequency financial data by proposing some appropriate statistical models and methods. These are interesting and challenging problems in statistical modeling and also very useful in finance. We will study these special characteristics, consider methods for analyzing high frequency data, and discuss the potential application of the results obtained. In particular, we propose method for mod

英文关键词： high-frequency data；self-weighted integrated co-volatility；semi-martingale model；endogeneity；high-dimensional data