中国股市的运行状态研究——基于修正的马尔可夫转换模型被引量：3

Identification of States in China Stock Market: Based on Markov-switching Model

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摘要在非有效市场中,股市的波动会表现出周期性特征。为深入研究中国股票市场的运行状态及各状态间的转换规律,本文构建马尔可夫转换模型,并借助贝叶斯方法进行相关参数估计以及最优状态数目的确定。以上证综指为对象的实证检验结果表明,中国的股市自建立以来,可以清楚地划分为"稳定牛市"、"震荡牛市"、"鹿市"、"熊市"和"极度波动"这5个状态。在不同状态内部,股市运行呈现出不同的特征,每一状态持续的时间以及向其他状态转换的概率也不相同。 This paper develops a Bayesian approach of Markov-switching model to study the states of mean and volatility of stock index returns, the optimal number of states, and transitions between states. An empirical study based on the Shanghai Composite Index shows that there are five distinct states in China＇s stock market, which are steady bull market, fluctuating bull market, deer market, bear market and extremely volatile market. The stock market experiences different characteristic in each state, while the duration of each state and the probability of transition to other states varies.

作者蒋彧方先明

机构地区南京大学经济学院

出处《中国经济问题》 CSSCI 北大核心 2015年第3期63-73,共11页 China Economic Studies

基金国家自然科学基金(编号:71301072) 教育部博士点基金(编号:20120091120001) 中国博士后科学基金特别资助(编号:2014T70494) 国家社会科学基金(编号:14BGL031) 江苏省哲学社会科学基金重点项目(项目号:13YEA001)的资助

关键词马尔可夫转换模型贝叶斯方法股指收益率状态识别 Markov-switching model Bayesian approach stock index returns identification of states

分类号 F832.51 [经济管理—金融学] F224 [经济管理—国民经济]

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