Efficient Quasi-Bayesian Estimation of Affine Option Pricing Models Using Risk-Neutral Cumulants

Riccardo Brignone, Luca Gonzato and Eva Lütkebohmert

Journal of Banking & Finance, 2023, vol. 148, issue C

Abstract: We propose a general, accurate and fast econometric approach for the estimation of affine option pricing models. The algorithm belongs to the class of Laplace-Type Estimation (LTE) techniques and exploits Sequential Monte Carlo (SMC) methods. We employ functions of the risk-neutral cumulants given in closed form to marginalize latent states, and we address parameter estimation by designing a density tempered SMC sampler. We test our algorithm on simulated data by tackling the challenging inference problem of estimating an option pricing model which displays two stochastic volatility factors, allows for co-jumps between price and volatility, and stochastic jump intensity. Furthermore, we consider real data and estimate the model on a large panel of option prices. Numerical studies confirm the accuracy of our estimates and the superiority of the proposed approach compared to its natural benchmark.

Keywords: Sequential Monte Carlo; Quasi-Bayesian Estimation; Risk-Neutral Cumulants; Multifactor Affine Models (search for similar items in EconPapers)
JEL-codes: C13 C15 C58 G12 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:148:y:2023:i:c:s0378426622003259

DOI: 10.1016/j.jbankfin.2022.106745

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