Structural Change as an Alternative to Long Memory in Financial Time Series
نویسندگان
چکیده
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 in which 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. JEL Classifications: C11, C13, C22. Corresponding author’s (Lai’s) address: Department of Statistics, Stanford University, Stanford, CA 94305-4065 Email: [email protected] Fax: 650-725-8977
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