Distribution switching in financial time series

A new method for detecting regime switches between different probability distributions in financial time series is shown. In the proposed method, time series observations are divided into several segments, and a Gaussian model or a Cauchy model is fitted to each segment. The goodness of fit of the global model composed of these local models is evaluated using the Bayesian information criterion (BIC), and the division which minimizes this criterion defines the best model. Based on this method, for example, the specification with a Gaussian process in the first half and with a Cauchy process in the second half becomes applicable. Empirical applications and data-based simulations are presented to indicate the efficacy of the proposed method.