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Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

Author

Listed:
  • Shelton Peiris

    (University of Sydney, Australia)

  • Manabu Asai

    (Soka University, Japan)

  • Michael McAleer

    (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University of Madrid, Spain)

Abstract

In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.

Suggested Citation

  • Shelton Peiris & Manabu Asai & Michael McAleer, 2016. "Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models," Tinbergen Institute Discussion Papers 16-044/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20160044
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    Cited by:

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    More about this item

    Keywords

    Stochastic volatility; GARCH models; Gegenbauer Polynomial; Long Memory; Spectral Likelihood; Estimation; Forecasting;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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