Implied Filtering Densities on the Hidden State of Stochastic Volatility

Carlos Fuertes, Andrew Papanicolaou

Research output: Contribution to journalArticlepeer-review


Abstract: We formulate and analyse an inverse problem using derivative prices to obtain an implied filtering density on volatility’s hidden state. Stochastic volatility is the unobserved state in a hidden Markov model (HMM) and can be tracked using Bayesian filtering. However, derivative data can be considered as conditional expectations that are already observed in the market, and which can be used as input to an inverse problem whose solution is an implied conditional density on volatility. Our analysis relies on a specification of the martingale change of measure, which we refer to as separability. This specification has a multiplicative component that behaves like a risk premium on volatility uncertainty in the market. When applied to SPX options data, the estimated model and implied densities produce variance-swap rates that are consistent with the VIX volatility index. The implied densities are relatively stable over time and pick up some of the monthly effects that occur due to the options’ expiration, indicating that the volatility-uncertainty premium could experience cyclic effects due to the maturity date of the options.

Original languageEnglish (US)
Pages (from-to)483-522
Number of pages40
JournalApplied Mathematical Finance
Issue number6
StatePublished - Jan 1 2014


  • Filtering
  • HMM
  • Heston model
  • stochastic volatility
  • volatility risk premium
  • volatility uncertainty

ASJC Scopus subject areas

  • Finance
  • Applied Mathematics


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