Fast Filtering with Large Option Panels: Implications for Asset Pricing

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Relatore
Jeroen Rombouts - ESSEC Business School
Data e ora
mercoledì 16 novembre 2022 alle ore 12.00 - In presenza
Referente
Roberto Renò
Data pubblicazione
10 settembre 2022
Dipartimento
Scienze Economiche  

Riassunto

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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