2024/08/03 by Jimin Lin, Lin, Jimin, Guixin Liu +1 · 1 citation
Computer Science · #Computational Finance (q-fin.CP) #Computer science #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Mathematical Finance (q-fin.MF) #Neural Networks and Applications #Physics #Pricing of Securities (q-fin.PR) #Process (computing) #Term (time)
paper · pdf · doi:10.48550/arxiv.2408.01642
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/08/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The additive process generalizes the Lévy process by relaxing its assumption of time-homogeneous increments and hence covers a larger family of stochastic processes. Recent research in option pricing shows that modeling the underlying log price with an additive process has advantages in easier construction of the risk-neural measure, an explicit option pricing formula and characteristic function, and more flexibility to fit the implied volatility surface. Still, the challenge of calibrating an additive model arises from its time-dependent parameterization, for which one has to prescribe parametric functions for the term structure. For this, we propose the neural term structure model to utilize feedforward neural networks to represent the term structure, which alleviates the difficulty of designing parametric functions and thus attenuates the misspecification risk. Numerical studies with S&P 500 option data are conducted to evaluate the performance of the neural term structure.