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This paper provides a Bayesian test of parameter nonstationarity and an estimation procedure for the detection of change points in the time series of stock returns. The empirical results indicate that this procedure can identify the change points in the data without prior knowledge and provide substantially more descriptive validity for the distribution of stock returns than competing models

Sequential parameter nonstationarity in stock market returns