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arXiv · 1512.08381

Inferring Volatility in the Heston Model and its Relatives -- an Information Theoretical Approach

Nils Bertschinger·Oliver Pfante
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Abstract

Stochastic volatility models describe asset prices $S_t$ as driven by an unobserved process capturing the random dynamics of volatility $\sigma_t$. Here, we quantify how much information about $\sigma_t$ can be inferred from asset prices $S_t$ in terms of Shannon's mutual information $I(S_t : \sigma_t)$. This motivates a careful numerical and analytical study of information theoretic properties of the Heston model. In addition, we study a general class of discrete time models motivated from a machine learning perspective. In all cases, we find a large uncertainty in volatility estimates for quite fundamental information theoretic reasons.

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BibTeXRIS

Nils Bertschinger, Oliver Pfante. 2015-12-28. Inferring Volatility in the Heston Model and its Relatives -- an Information Theoretical Approach. https://arxiv.org/abs/1512.08381

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