Abstract
It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is an important task in financial time series analysis. Change point detection is the identification of abrupt changes in the generative parameters of sequential data. In application areas such as finance, online rather than offline detection of change points in time series is mostly required, due to their use in predictive tasks, possibly embedded in automatic trading systems. However, the complex structure of the data generation processes makes this a challenging endeavor. This paper is concerned with online change point detection in financial time series using the Bayesian setting. To this end, the Bayesian posterior probability of change at a specific time is proposed and some procedures are presented for selecting the priors and estimation of parameters. Applications in simulated financial time series are given. Finally, conclusions are proposed.