关键词: 贝叶斯, 变点, 上证指数

Abstract: This article uses a Bayesian procedure to study structure changes of both mean and variance in stock price time series. A fast algorithm like ICSS algorithm is proposed to detect change points of both mean and variance based on posterior odds. We discover five changes of variance in a Shanghai composite index time series. Three of them are change points of both mean and variance that indicate significant structure changes in Chinese stock market.

Key words: Bayesian, Change Points, Shanghai Composite Index