PB-GARCH模型在中国股市收益波动率研究中的应用

THE APPLICATION OF PB-GARCH MODEL ON THE VOLATILITY OF CHINESE STOCK MARKET

股票市场的波动率一直是金融学研究的热点问题,由于受到宏观经济运行状况、行业自身发展状况以及投资者心理变化情况的影响,股票价格的收益率呈现出复杂的非线性波动,收益的方差是自相关的,表现出分形和混沌的特征．随着股票市场研究的逐步深入,人们发展了以ARCH族模型和随机波动模型(SV)为代表的波动预测模型．但是,目前这些预测模型大多仅从股价自身的运行规律进行研究,很少考虑投资者的心理因素对股价运行规律的影响．本文结合行为金融的前景理论、GARCH模型,以及神经网络等理论,建立基于投资者心理变化的波动率模型,对该模型的估计方法进行了探讨,并以我国上证综合指数(HSEC)和深证成份指数(ZSEC)为例进行了实证分析．

The volatility of stock market is currently a hot issue discussed in financial researches.Affected by macroeconomic conditions,the development of industries and the diversity of investors'psychology,the returns of stock prize appear to have some characters like complex non-linear volatility and autocorrelation of return' s variance as well as fractal and chaotic features.Since the researches of stock market are deepened,many volatility forecasting models have been developed,such as ARCH(Autoregressive Conditional Heteroskedasticity)models and SV(Stochastic Volatility)models.However,most of those models focus on the running rules of stock prizes rather than the effect of investors'psychological factors on such rules.This paper combines Prospect Theory in Behavior Finance,GARCH models and Neural Network,puts forward a volatility model based on the change of investors'psychology,discusses its estimation and tests it by some empirical analysis on Shanghai Composite Index and Shenzhen SSE Composite Index.