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Volatility Inference and Return Dependencies in Stochastic Volatility Models

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  • Oliver Pfante
  • Nils Bertschinger

Abstract

Stochastic volatility models describe stock returns $r_t$ as driven by an unobserved process capturing the random dynamics of volatility $v_t$. The present paper quantifies how much information about volatility $v_t$ and future stock returns can be inferred from past returns in stochastic volatility models in terms of Shannon's mutual information.

Suggested Citation

  • Oliver Pfante & Nils Bertschinger, 2016. "Volatility Inference and Return Dependencies in Stochastic Volatility Models," Papers 1610.00312, arXiv.org.
    • Handle: RePEc:arx:papers:1610.00312
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    References listed on IDEAS

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    Cited by:

    1. Oliver Pfante & Nils Bertschinger, 2016. "Uncertainty Estimates in the Heston Model via Fisher Information," Papers 1610.04760, arXiv.org, revised Oct 2016.

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