Volatility Inference and Return Dependencies in Stochastic Volatility   Models
  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. 
Oliver Pfante and Nils Bertschinger
q-fin.MF