Structural Change as an Alternative to Long Memory in Financial Time Series

Abstract

This paper shows that volatility persistence in GARCH models and spurious long memory in autoregressive models may arise if the possibility of structural changes is not incorporated in the time series model. It also describes a tractable hidden Markov model (HMM) in which the regression parameters and error variances may undergo abrupt changes at unknown time points, while staying constant between adjacent change-points. Applications to real and simulated financial time series are given to illustrate the issues and methods.

Citation

Leung Lai, T. and Xing, H. (2006), "Structural Change as an Alternative to Long Memory in Financial Time Series", Fomby, T.B. and Terrell, D. (Ed.) Econometric Analysis of Financial and Economic Time Series (Advances in Econometrics, Vol. 20 Part 2), Emerald Group Publishing Limited, Leeds, pp. 205-224. https://doi.org/10.1016/S0731-9053(05)20027-0