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Estimating persistence in the volatility of asset returns with signal plus noise models

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  • Guglielmo Maria Caporale
  • Luis A. Gil‐Alana

Abstract

This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by Robinson (1995a), and shown by Arteche (2004) to be consistent and asymptotically normal in the context of signal plus noise models. Daily data on the NASDAQ index are analysed. The results suggest that volatility has a component of long- memory behaviour, the order of integration ranging between 0.3 and 0.5, the series being therefore stationary and mean-reverting.
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Suggested Citation

  • Guglielmo Maria Caporale & Luis A. Gil‐Alana, 2012. "Estimating persistence in the volatility of asset returns with signal plus noise models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 23-30, January.
    • Handle: RePEc:wly:ijfiec:v:17:y:2012:i:1:p:23-30
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    Cited by:

    1. Battaglia, Francesca & Buchanan, Bonnie G. & Fiordelisi, Franco & Ricci, Ornella, 2021. "Securitization and crash risk: Evidence from large European banks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    2. Aliyev, Fuzuli & Ajayi, Richard & Gasim, Nijat, 2020. "Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    3. Fiordelisi, Franco & Ricci, Ornella & Santilli, Gianluca, 2023. "Environmental engagement and stock price crash risk: Evidence from the European banking industry," International Review of Financial Analysis, Elsevier, vol. 88(C).

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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