基于高频数据的中国股市VaR风险研究.doc

基于高频数据的中国股市 VaR 风险研究
重庆大学硕士学位论文
(学术学位)
学生姓名:伍****丽
指导教师:傅强教授
专业:应用数学
学科门类:理学
重庆大学数学与统计学院
二 O 一三年四月
Research on VaR of Chinese Stock Market
based on High Frequency Data
A Thesis Submitted to Chongqing University
in Partial Fulfillment of the Requirement for the
Master’s Degree of Science
By
Wu Xili
Supervised by Prof. Fu Qiang
Specialty:Applied Mathematics
College of Mathematics and Statistics of Chongqing University,
Chongqing, China
April 2013
重庆大学硕士学位论文中文摘要
摘要
金融市场的风险度量一直是学术界和风险监管当局关注的重点。传统的风险
度量大多数都是基于低频日间数据建立的GARCH类模型或SV类模型。虽然这些模
型本身能较好的度量时间序列的波动状况,但股市日内交易频繁,低频数据模型
会损失大量的日内重要信息。现有研究表明,传统的GARCH类模型并不能直接用
于估计高频波动率。建立有效的高频数据风险度量模型,为金融机构和监管当局
的风险监控提供一种有效的理论方法参考和政策建议具有重大意义。
本文结合前人对已实现类高频波动率的研究,对已实现波动率RV、已实现双
幂次波动率 RBV 和赋权已实现双幂次波动率 WRBV 进行比较,针对 WRBV 具有
的长记忆性,建立了 ARFIMA-WRBV-VaR 模型对中国股市风险进行度量,并与采
用低频日间收益率序列建立的 GARCH 类模型相比较。
实证结果表明:ARFIMA- WRBV- VaR 模型比 EGARCH-VaR 模型估计效果更
好。而且,已实现类高频波动率出现了跳跃点、日内 U 型周期性日历效应和长记
忆性特征,这些特征受市场微观结构中的信息不对称和投资者心理等因素影响。
进而为风险监控提出了完善信息披露机制和增强投资者素质的政策建议。
关键词:VaR,高频波动率,ARFIMA 模型,GARCH 模型,微观结构
I
重庆大学硕士学位论文英文摘要
ABSTRACT
The academic and financial regulatory authorities always focus on the risk
measurement of financial market. Most of traditional risk measurements are using
GARCH model or SV model based on low-frequency intraday data. Although these
models can measure the volatility of time series well, because of the stock market
transaction frequently in every day, low-frequency data model will loss a large number
of daily important information. The current study shows that, the traditional GARCH
models cannot be directly used to estimate high-frequency vo lat ilit y. It is significant to
establish an efficiency risk measurement model based on high- frequency data. So that
we can provide an effective theory method and policy mendations for risk control
of financial institutions and regulatory authorities.
Based on previous research on realized high-frequency volatility ,this paper
compares realized vo