Work

Option pricing mechanisms driven by backward stochastic differential equations

Year: 2025

Type: article

Abstract: This study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning. We adopted a data-driven approach to find a market-appropriate generator of the backward stochastic differential equation, which is achieved by leveraging the universal approximation capabilities of neural networks. Option pricing, which is the solution to the equation, is approximated using a recursive procedure. The empirical results for the S&P 500 index options show that the proposed deep learning g-pricing model has lower absolute errors than the classical Black–Scholes–Merton model for the same forward stochastic differential equations. The g-pricing mechanism has potential applications in option pricing.

Source: Financial Innovation

DOI: 10.1186/s40854-024-00714-3

Authors: Yufeng Shi, Bin Teng, Sicong Wang

Institution: Shandong University

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: diamond

Funders: Taishan Scholar Project of Shandong Province, National Key Research and Development Program of China

Awards: G1150564231, G1711763591