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Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

Author

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  • Shelton Peiris

    (School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia)

  • Manabu Asai

    (Faculty of Economics, Soka University, Tokyo 192-8577, Japan)

  • Michael McAleer

    (Department of Quantitative Finance, National Tsing Hua University, Hsinchu 300, Taiwan
    Discipline of Business Analytics, University of Sydney Business School, Darlington, NSW 2006, Australia
    Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands
    Department of Quantitative Economics, Complutense University of Madrid, 28040 Madrid, Spain)

Abstract

This paper considers a flexible class of time series 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 corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.

Suggested Citation

  • Shelton Peiris & Manabu Asai & Michael McAleer, 2017. "Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models," JRFM, MDPI, vol. 10(4), pages 1-16, December.
    • Handle: RePEc:gam:jjrfmx:v:10:y:2017:i:4:p:23-:d:122610
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    2. Shang, Yuhuang & Zheng, Tingguo, 2021. "Mixed-frequency SV model for stock volatility and macroeconomics," Economic Modelling, Elsevier, vol. 95(C), pages 462-472.

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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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