Fast Filtering with Large Option Panels: Implications for Asset Pricing

Jeroen Rombouts - ESSEC Business School

Date and time
Wednesday, November 16, 2022 at 12:00 PM - In presenza

Contact person
Roberto RenĂ²

Publication date
September 10, 2022



The cross-section of options holds great promise for identifying return distributions and risk premia, 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 filtering approach and illustrate our method by estimating index option pricing models. Estimates of variance risk premiums, variance mean reversion, and higher moments differ from the literature. We show that these differences are due to the composition of the option sample. Restricting the option sample's maturity dimension has the strongest impact on parameter inference and option fit in these models.

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