报告题目：深度算子网络对于列维模型下欧式期权价格函数的表示性分析

报告人：张功球助理教授(香港中文大学（深圳）)

时间：2025年9月17日下午4:00-5:30

地点：西安交通大学兴庆校区主楼C-106

报告人简介：

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。

摘要：

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.