Journal of Statistical and Econometric Methods

Robust Estimation of the Memory Parameter

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  • Abstract

    Recent studies have found indications of long-range dependence in financial time series and used conventional, non-robust estimates of the memory parameter, which measures the degree of long-range dependence, for the calculation of buy and sell signals. In this paper, new robust estimators are proposed which are possibly more appropriate for financial data. The new estimators are compared with various robust and non-robust competitors by means of extensive simulations. In addition to additive outliers and heavy-tailed distributions, also conditional heteroscedasticity is considered. The results show that the robust estimators do not generally deliver better results than the conventional estimators but only in special cases, the existing robust estimators with respect to the root-mean-square error and the new robust estimators with respect to the bias. Finally, the different estimators are used to investigate possible long-range dependence both in developed and developing stock markets.  The results of this empirical study suggest that long-range dependence is present only in the volatility and is therefore of no use for directional forecasting and trading.

    JEL classification numbers: C13, C14, C22, C58, G15

    Keywords: Long-range dependence, Frequency-domain estimation, Periodogram, Truncated F-distribution, Volatility, Stock markets.