The cross-section of options holds great promise for identifying return distributions and risk premiums, but estimating dynamic option valuation models with latent state
variables is challenging when using large option panels. We propose a particle MCMC framework with a novel ltering approach and illustrate our method by estimating
workhorse index option pricing models. Estimates of the variance risk premium, variance mean reversion, and higher moments differ from the literature. We show that
these differences are due to the composition of the option sample. Restrictions on the option sample's maturity dimension have the strongest impact on parameter inference
in these models.