91自拍 学术报告[2025]151号

(高水平大学建设系列报告1252号)

报告题目：Deep Operator Network Expressivity for European Option Pricing under Exponential Levy Models

报告人：张功球 助理教授 香港中文大学（深圳）

报告时间：2025年12月9日下午14:30-15:30

报告地点：91自拍 粤海校区汇星楼514

报告内容：In this paper, we obtain the expression rates of the deep operator network (DeepONet) for learning the pricing operator that maps from the space of coefficient functions to that of pricing functions for European options under exponential time-inhomogeneous L´evy models. Under some structural assumptions on the payoff function, we show that DeepONet overcomes the curse of dimensionality for this problem, i.e., it can achieve an arbitrary uniform error of ε > 0 with the network size growing polynomially in the number of underlying assets (d) and 1/ε. With another set of assumptions on the payoff, we show that the error of DeepONet can decay exponentially in its size, albeit with the implied constant possibly growing exponentially in d. This work is joint with Lingfei Li, Yeda Cui and Wenyong Zhang.

报告人简历：张功球，香港中文大学（深圳）助理教授、博士生导师、深圳市大数据研究院研究科学家。主要研究金融数学、金融科技、计算金融等方向。研究成果发表于 Operations Research, Mathematical Finance, Finance and Stochastics, Journal of Economic Dynamics and Control, SIAM Journal on Financial Mathematics, SIAM Journal on Scientific Computing等期刊，主持多项国家自然科学基金与深圳市科创委项目。

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邀请人：李婧超

91自拍

2025年12月8日