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Appraising Model Complexity in Option Pricing

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  • Mark Cummins
  • Francesco Esposito

Abstract

The research question we consider is whether incremental complexity in option pricing models is justified by incremental model performance. We apply the model confidence set as a formal model comparison approach in appraising stochastic volatility jump‐diffusion option pricing models, spanning affine and nonaffine specifications. Jumps in price with stochastic (constant) arrival intensity produce superior (inferior) outcomes. A parsimonious negative exponential price jump distribution outperforms the popular normal distribution. Jumps in volatility (synchronized or not) worsen model performance. A parsimonious nonlinear hyperbolic drift extension of the Heston model performs particularly well. Nonlinear CEV models generally do not produce appreciable model performance.

Suggested Citation

  • Mark Cummins & Francesco Esposito, 2025. "Appraising Model Complexity in Option Pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(5), pages 455-472, May.
    • Handle: RePEc:wly:jfutmk:v:45:y:2025:i:5:p:455-472
    DOI: 10.1002/fut.22575
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    Cited by:

    1. Duosi Zheng & Hanzhong Guo & Yanchu Liu & Wei Huang, 2025. "Neural Jumps for Option Pricing," Papers 2506.05137, arXiv.org.

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