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Dependence properties of stochastic volatility models

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  • Piotr Kokoszka
  • Neda Mohammadi
  • Haonan Wang

Abstract

The concepts of physical dependence and approximability have been extensively used over the past two decades to quantify nonlinear dependence in time series. We show that most stochastic volatility models satisfy both dependence conditions, even if their realizations take values in abstract Hilbert spaces, thus covering univariate, multi‐variate and functional models. Our results can be used to apply to general stochastic volatility models a multitude of inferential procedures established for Bernoulli shifts.

Suggested Citation

  • Piotr Kokoszka & Neda Mohammadi & Haonan Wang, 2025. "Dependence properties of stochastic volatility models," Journal of Time Series Analysis, Wiley Blackwell, vol. 46(3), pages 421-431, May.
    • Handle: RePEc:bla:jtsera:v:46:y:2025:i:3:p:421-431
    DOI: 10.1111/jtsa.12765
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