Long Memory Process in Asset Returns with Multivariate GARCH Innovations | Zendy

Imène Mootamri | Zendy

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Long Memory Process in Asset Returns with Multivariate GARCH Innovations

Author(s) -

Imène Mootamri

Publication year - 2011

Publication title -

economics research international

Language(s) - English

Resource type - Journals

eISSN - 2090-2123

pISSN - 2090-2131

DOI - 10.1155/2011/564952

Subject(s) - econometrics , multivariate statistics , autoregressive fractionally integrated moving average , stock (firearms) , economics , autoregressive conditional heteroskedasticity , volatility (finance) , financial economics , long memory , mathematics , statistics , mechanical engineering , engineering

The 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 confirmthe presence of long memory property in the conditional mean of all stock returns

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