Dynamic local models for segmentation and prediction of financial time series

Abstract

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. Aspecial form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.

Keywords:

• Time Series Segmentation Hidden Variational Techniques Bayesian Error Bars Markov Models State Space Models