Long Memory Process in Asset Returns with Multivariate GARCH innovations
AbstractThe main purpose of this paper is to consider the multivariate GARCH (MGARCH) framework to model the volatility of a multivariate process exhibiting long term dependence in stock returns. More precisely, the long term dependence is examined in the first conditional moment of US stock returns through multivariate ARFIMA process and the time-varying feature of volatility is explained by MGARCH models. An empirical application to the returns series is carried out to illustrate the usefulness of our approach. The main results confi rm the presence of long memory property in the conditional mean of all stock returns.
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Date of creation: 09 Jun 2011
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Forecasting; Long memory; Multivariate GARCH; Stock Returns;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-06-18 (All new papers)
- NEP-ECM-2011-06-18 (Econometrics)
- NEP-ETS-2011-06-18 (Econometric Time Series)
- NEP-FOR-2011-06-18 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gil-Alana, L. A., 2003. "A fractional multivariate long memory model for the US and the Canadian real output," Economics Letters, Elsevier, vol. 81(3), pages 355-359, December.
- John T. Barkoulas & Christopher F. Baum & Nickolaos Travlos, 1996.
"Long Memory in the Greek Stock Market,"
Boston College Working Papers in Economics
356., Boston College Department of Economics.
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