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Robust Estimation of the Memory Parameter

Author

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  • Erhard Reschenhofer
  • Thomas Stark
  • Manveer K. Mangat

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

Suggested Citation

  • Erhard Reschenhofer & Thomas Stark & Manveer K. Mangat, 2020. "Robust Estimation of the Memory Parameter," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(4), pages 1-5.
    • Handle: RePEc:spt:stecon:v:9:y:2020:i:4:f:9_4_5
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    References listed on IDEAS

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    More about this item

    Keywords

    Long-range dependence; Frequency-domain estimation; Periodogram; Truncated F-distribution; Volatility; Stock markets.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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