预测沪深股市市场波动性  

Forecasting the Volatility of the Shanghai and the Shenzhen Stock Markets

作　　者：郑梅[1] 苗佳 王升[1] 

机构地区：[1]华北科技学院管理系,北京101601 [2]约翰摩尔大学商学院

出　　处：《系统工程理论与实践》2005年第11期41-45,共5页Systems Engineering-Theory & Practice

摘　　要：研究目的在于:首先,检验和对比三种GARCH模型对于我国上海、深圳股市波动性的预测能力.其次,使用非对称的预测指标以体现对于预测误差高低的不同个体需求.在对于4个对称型的预测常规指标中,TGARCH对于2个市场波动的预测结果都是最佳的,而EGARCH的预测结果也要好于GARCH(1,1).这表明我国上海与深圳市场受坏消息的负面影响大于同等程度好消息的正面影响,而运用单边非对称的GARCH模型将更利于提高波动性预测的准确性.在非对称预测指标方面,研究认为EGARCH模型对于那些更愿意低估市场波动性的投资者而言较为有利,而GARCH(1,1)模型则相应满足了希望高估市场波动性的投资者的需求.这也表明投资者应根据自己的需要来选择相应的波动性预测模型.The objectives of this paper are twofold. Firstly, we evaluate the forecasting performance of GARCH （1,1）, TGARCH and EGARCH models of the Shanghai and the Shenzhen stock market volatility. Secondly, since not an investors assign equal weight to similar sized overpredictions and underpredictions, in addition to the conventional symmetric criteria considered, we also propose error statistics that designed to account for asymmetry in the loss function. Our results show that in terms of RMSE, MAE, MAPE and Theil-U criteria considered, TGARCH models perform the best in forecasting market volatility in both markets, while EGARCH models outperform regular GARCH （1,1） models. This suggests that ＂bad＂ news has a significantly stronger impact on market volatility than ＂good＂ news of the same size, and that non-linear GARCH models are able to explain the Chinese market volatility better than linear GARCH models. As for the 2 asymmetric criteria, our results show that in both markets EGARCH models outperform in terms of MME（U） criterion, which penalize underpredictions more heavily, while GARCH （1,1） models performs the best in terms of MME （O） when overpredictions are penalized more heavily. This suggests that investors should select the appropriate volatility forecasting models to tailor their individual requirements.

关 键 词：波动性 条件方差 预测 GARCH模型 非对称衡量指标 

分 类 号：F830.593[经济管理—金融学]