Multiple break detection in the correlation structure of financial returns

Abstract

Correlations between asset returns plays an important role in financial analysis. More precisely, accurate estimates of the correlation between financial returns are crucial in portfolio management. In particular, in periods of fi nancial crisis, extreme movements in asset prices are found to be more highly correlated than small movements. It is precisely under these conditions that investors are extremely concerned about changes on correlations. We propose a sequential procedure to detect the number and position of multiple change points in the correlation structure of financial returns. It is shown analytically that the proposed algorithm asymptotically gives the correct number of change points and the change points are consistently estimated. It is also shown by simulation studies and by an empirical application that the algorithm yields reasonable results.