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

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.& COPY; 2021 Elsevier B.V. All rights reserved.

Automated summary

This article presents a quadratic hedging framework for a general class of discrete-time affine multi-factor models and investigates the impact of various modeling features on the hedging effectiveness of S&P 500 options. The study finds that fat tails, a second volatility factor, and a non-monotonic pricing kernel contribute to the hedging improvement, with fat tails accounting for half of the improvement. Additionally, the study reveals that the added value of these features for hedging differs from their value for pricing, and similar conclusions hold when considering the Dow Jones Industrial Average.