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Fast Filtering with Large Option Panels: Implications for Asset Pricing.

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Titel: Fast Filtering with Large Option Panels: Implications for Asset Pricing.
Autoren: Dufays, Arnaud1 (AUTHOR) arnaud.dufays@edhec.edu, Jacobs, Kris2 (AUTHOR) kjacobs@bauer.uh.edu, Liu, Yuguo3 (AUTHOR) yuguo.liu@morganstanley.com, Rombouts, Jeroen4 (AUTHOR) rombouts@essec.edu
Quelle: Journal of Financial & Quantitative Analysis. Nov2024, Vol. 59 Issue 7, p3416-3447. 32p.
Schlagwörter: *OPTIONS (Finance), *ASSETS (Accounting), *PRICING, *VALUATION, *MONTE Carlo method
Abstract: 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 Markov Chain Monte Carlo 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. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
Abstract: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 Markov Chain Monte Carlo 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. [ABSTRACT FROM AUTHOR]
ISSN:00221090
DOI:10.1017/S0022109023000753