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- W2203371118 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." @default.
- W2203371118 created "2016-06-24" @default.
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- W2203371118 date "2015-12-28" @default.
- W2203371118 modified "2023-09-27" @default.
- W2203371118 title "Inferring Volatility in the Heston Model and its Relatives -- an Information Theoretical Approach" @default.
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