Fast Filtering with Large Option Panels: Implications for Asset Pricing

Jeroen Rombouts - ESSEC Business School

Data e ora
mercoledì 16 novembre 2022 alle ore 12.00 - In presenza

Roberto Renò

Data pubblicazione
10 settembre 2022

Scienze Economiche  


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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