Volatility Estimation of Shanghai and Shenzhen Stock Market Based on Markov Regime Switching Models
Volatility Estimation of Shanghai and Shenzhen Stock Market Based on Markov Regime Switching Models
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发表时间：
2013
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通讯作者：
Zhang Chun-hui
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作者：
Zhang Chun-hui
为了更准确地估计沪深股市日收益序列波动性，本文将沪深股市日收益序列的波动性分为上涨、下跌和盘整三种状态。选取上证综指和深成指收益序列作为研究样本，设定2000年1月4日至2011年12月30日为样本期，2012年1月4日至2012年1月17日为样本期。然后应用GARCH模型、RS-GARCH模型、APGARCH模型和RS-APGARCH模型对这两个收益序列的波动性进行估计和预测。最后利用MSE1、MSE2和QLIKE对这些模型的性能进行评价。结果表明APGARCH模型对序列波动性的估计和预测比GARCH模型更准确，马尔可夫状态切换模型的估计和预测更准确正态误差分布的模型比误差服从t分布的模型对序列波动性的估计和预测更加准确。
In order to get the more accurate estimation of volatility of daily return series of Shanghai and Shenzhen Stock market with regime switching,volatility of these stock index return series are divided into three regime states: rising,falling and consoliclation in the paper.Return series of Shanghai Composite Index and Shenzhen Component Index are chosen as study sample and January 4,2000 to 2011 December 30 is set as the sample period and January 4,2012 to January 17,2012 is set as out of sample period.Then GARCH model,RS-GARCH model,APGARCH model and RS-APGARCH model are applied to estimation and forecasting of volatility of these two return series.Finally MSE1,MSE2 and QLIKE are used to evaluate the performance of these models.The results show that APGARCH model is more accurate in estimation and prediction of the volatilities of the series than the GARCH model,models with Markov regime switching are more accurate in estimation and prediction of the volatilities of the series,and the models with normal error distribution are more accurate in estimation and prediction of the volatilities of the series than the models with the error distribution following t-distribution.