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Long memory persistence in the factor of Implied volatility dynamics

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  • Härdle, Wolfgang Karl
  • Mungo, Julius

Abstract

The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied Volatility Surface (IVS). Practical applications require reducing the dimension and characterize its dynamics through a small number of factors. Such dimension reduction is summarized by a Dynamic Semiparametric Factor Model (DSFM) that characterizes the IV S itself and their movements across time by a multivariate time series of factor loadings. This paper focuses on investigating long range dependence in the factor loadings series. Our result reveals that shocks to volatility persist for a very long time, affecting significantly stock prices. For appropriate representation of the series dynamics and the possibility of improved forecasting, we model the long memory in levels and absolute returns using the class of fractional integrated volatility models that provide flexible structure to capture the slow decaying autocorrelation function reasonably well.

Suggested Citation

  • Härdle, Wolfgang Karl & Mungo, Julius, 2007. "Long memory persistence in the factor of Implied volatility dynamics," SFB 649 Discussion Papers 2007-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Handle: RePEc:zbw:sfb649:sfb649dp2007-027
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    References listed on IDEAS

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    3. Ranjit Kumar Paul & Bishal Gurung & Sandipan Samanta, 2015. "Analyzing the Effect of Dual Long Memory Process in Forecasting Agricultural Prices in Different Markets of India," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(4), pages 235-249.
    4. Perederiy, Volodymyr, 2007. "Kombinierte Liquiditäts- und Solvenzkennzahlen und ein darauf basierendes Insolvenzprognosemodell für deutsche GmbHs," SFB 649 Discussion Papers 2007-060, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

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    More about this item

    Keywords

    Implied Volatility; Dynamic Semiparametric Factor Modeling; Long Memory; Fractional Integrated Volatility Models;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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