Option Pricing and Local Volatility Surface by Physics-Informed Neural Network

被引：0

作者：

Bae, Hyeong-Ohk ^{[1
]}

Kang, Seunggu ^{[2
]}

Lee, Muhyun ^{[3
]}

机构：

[1] Ajou Univ, Dept Financial Engn, Suwon 16499, South Korea

[2] Korea Asset Pricing & Korea Ratings, Seoul, South Korea

[3] Samsung Secur, Seoul, South Korea

来源：

COMPUTATIONAL ECONOMICS | 2024年 / 64卷 / 5期

基金：

新加坡国家研究基金会;

关键词：

Option pricing; Local volatility; Artificial neural network; Black-Scholes equation (BSE); Physics-informed neural network (PINN); Constant elasticity of variance (CEV); G13; C63; ALGORITHM; GREEKS;

D O I：

10.1007/s10614-024-10551-2

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We use an artificial neural network for finance in two directions: to estimate prices and Greeks based on the geometric Brownian motion and the constant elasticity of variance model for European options, and to construct a local volatility surface. To show the efficiency and successful usage of the network, we compare prices and Greeks obtained by a solution formula and by the artificial neural network when there is a solution formula is known. Then, we calculate Dupire's equations to construct a local volatility surface by the network.

引用

页码：3143 / 3159

页数：17

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