Structural Breaks, Incomplete Information, and Stock Prices
实证发现美国股价基本面存在结构性突变，并构建了一个马尔可夫转换模型，分析投资者在突变后如何逐步更新对股息的信念，从而解释股票收益的偏度、峰度、波动聚集和序列相关性。
AbstractThis article presents empirical evidence on the existence of structural breaks in the fundamentals process underlying U.S. stock prices. I develop an asset-pricing model that represents breaks in the context of a Markov switching process with an expanding set of nonrecurring states. Different hypotheses on how investors form expectations about future dividends after a break are proposed and analyzed. A model in which investors do not have full information about the parameters of the dividend process but gradually update their beliefs as new information arrives is shown to induce skewness, kurtosis, volatility clustering, and serial correlation in stock returns after a break.KEY WORDS: Asset pricingBayesian learningMarkov switchingVolatility