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Information-Theoretic Analysis Of Stochastic Volatility Models

Author

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  • OLIVER PFANTE

    (Frankfurt Institute for Advanced Studies, Systemic Risk Group, Frankfurt a. Main, Hesse 60438, Germany)

  • NILS BERTSCHINGER

    (Frankfurt Institute for Advanced Studies, Systemic Risk Group, Frankfurt a. Main, Hesse 60438, Germany)

Abstract

Stochastic volatility models describe asset prices St as driven by an unobserved process capturing the random dynamics of volatility σt. We quantify how much information about σt can be inferred from asset prices St in terms of Shannon’s mutual information in a twofold way: theoretically, by means of a thorough study of Heston’s model; from a machine learning perspective, by means of investigating a family of exponential Ornstein–Uhlenbeck (OU) processes fitted on S&P 500 data.

Suggested Citation

  • Oliver Pfante & Nils Bertschinger, 2019. "Information-Theoretic Analysis Of Stochastic Volatility Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-21, February.
    • Handle: RePEc:wsi:acsxxx:v:22:y:2019:i:01:n:s021952591850025x
    DOI: 10.1142/S021952591850025X
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    References listed on IDEAS

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