基于高频数据的金融市场间信息溢出与风险传染的微观机理、动态模型及其应用

项目名称： 基于高频数据的金融市场间信息溢出与风险传染的微观机理、动态模型及其应用

项目编号： No.71471182

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 管理科学

项目作者： 刘向丽

作者单位： 中央财经大学

项目金额： 60万元

中文摘要： 本研究利用高频数据对信息溢出和风险传染进行动态研究。首先利用小波分析和经验模式分解去噪，同时考虑测量误差与微观结构误差，研究最优采样频率问题，再提出改进的日内加权已实现波动率指标，对波动率进行更为精确的估计。然后引入独立成分分析法，剔除信息的共同影响，再引入跳变因子，构建带跳的时变多元模型，描述传染程度随时间的变化过程，考察不同金融市场间的信息传递。最后构建基于样条函数和互相关函数的双重权重的Granger因果检验统计量，既使用所有的滞后阶数，又克服了由于日内效应的存在使得不同时点高频数据的不可比等缺点，并采用新的参数估计方法，使之对高频数据更加稳健。从低频到高频不是简单的移植，从方法上讲，这是一项开创性的工作，为金融市场间风险传染研究提供新思路和新方向，将成为信息溢出效应和风险管理研究在高频领域的理论基础，为我国金融市场应对危机、保持稳定、健康发展提供实时量化支持，具有广泛的应用前景。

中文关键词： 风险溢出；非线性时间序列分析；Granger因果检验；市场微观结构；高频数据分析

英文摘要： This project studies the dynamics of the information spillover and risk contagion based on high frequency data.Taking into account both measurement errors and microstructural errors, we discuss the problem of optimal sample frequency after eliminating the noise by using wavelet analysis and empirical mode decomposition method. The intraday effect weighted realized volatility is improved to estimate the volatility more accurately. Then Independent Component Analysis (ICA) is introduced to eliminate the common effects of the new information, jump is added, and a time-varying multi-variable model with jump is built to describe the time-varying contagion and the information spillovers among different financial markets. Finally, a new double weighted Granger Causality test statistic, which is based on spline function and cross correlation function, is constructed. It is not only capable of considering all lag-orders but also suit for high frequency data. It can overcome the shortcoming of the incomparability of the high frequency data for the existences of intraday effects. And a new parameter estimation method is proposed to have a more robust understanding for high frequency data. The significance of this project is as follow: Theoretically, it is an innovation to extend the methodology from low frequency to high frequency in the area of information spillover. The findings can provide the fundamental basis for information spillover and risk management by high frequency data. Practically, it gives the time-varying quantitative support, which is of particular importance to the preparation of financial crisis and can promote the healthy and stable development of China's financial markets. It has wide applications.

英文关键词： Risk Spillover;Nonlinear Time Series Analysis;Granger Causality Test;Market Microstructure;High Frequency Data Analysis