Detecting Multiple Breaks in Time Series Covariance Structure: a Nonparametric Approach Based on the Evolutionary Spectral Density

This article estimates the number of breaks and their locations in the covariance structure of a series based on the evolutionary spectral density and uses some standard information criteria. The adopted approach is non-parametric and does hot privilege a priori any modelling of the series. One carries out a Monte Carlo analysis and an empirical illustration using the daily return series of exchange rate euro/US dollar to support the relevance of the theory and to produce additional insights. The simulation results are globally adequate and show that the criteria having heavy penalty are more accurate in the selection of the number of breaks. The empirical results indicate that the covariance structure of the return series considerably varies between 30 March 2000 and 6 April 2001. The unconditional volatility appears non-constant over this interval.