A discrete-time hedging framework with multiple factors and fat tails: On what matters

Maciej Augustyniak, Alexandru Badescu and Jean-François Bégin

Journal of Econometrics, 2023, vol. 232, issue 2, 416-444

Abstract: This article presents a quadratic hedging framework for a general class of discrete-time affine multi-factor models and investigates the extent to which multi-component volatility factors, fat tails, and a non-monotonic pricing kernel can improve the hedging performance. A semi-explicit hedging formula is derived for our general framework which applies to a myriad of the option pricing models proposed in the discrete-time literature. We conduct an extensive empirical study of the impact of modelling features on the hedging effectiveness of S&P 500 options. Overall, we find that fat tails can be credited for half of the hedging improvement observed, while a second volatility factor and a non-monotonic pricing kernel each contribute to a quarter of this improvement. Moreover, our study indicates that the added value of these features for hedging is different than for pricing. A robustness analysis shows that a similar conclusion can be reached when considering the Dow Jones Industrial Average. Finally, the use of a hedging-based loss function in the estimation process is investigated in an additional robustness test, and this choice has a rather marginal impact on hedging performance.

Keywords: Option hedging; Risk-minimization; Affine models; Multi-component volatility; Exponential-affine pricing kernels (search for similar items in EconPapers)
JEL-codes: C58 G12 G13 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:232:y:2023:i:2:p:416-444

DOI: 10.1016/j.jeconom.2021.08.002

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