Option Pricing

Option pricing, the process of determining the fair value of a financial contract granting the right, but not the obligation, to buy or sell an underlying asset at a specific price, aims to accurately predict option prices and manage associated risks. Current research heavily utilizes machine learning, employing various neural network architectures (including MLPs, RNNs like LSTMs and GRUs, and specialized networks like KANs and TDNNs) and reinforcement learning algorithms to improve pricing accuracy and efficiency, particularly for complex options like American options and those under data scarcity. These advancements offer more robust and potentially more accurate pricing models than traditional methods like the Black-Scholes model, impacting both academic quantitative finance and practical applications in risk management and investment strategies.

Papers

October 3, 2024

Diffusion Meets Options: Hierarchical Generative Skill Composition for Temporally-Extended Tasks
Zeyu Feng, Hao Luan, Kevin Yuchen Ma, Harold Soh
Robot Navigation Temporal Logic Hierarchical Reinforcement Learning Long Horizon Option Pricing Temporal Task Latent Skill

September 26, 2024

Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity
Qiguo Sun, Hanyue Huang, XiBei Yang, Yuwei Zhang
Neural Network Transfer Learning Deep Network Data Scarcity Quantitative Finance Option Pricing Independent Jump Black Scholes Option Pricing Model

September 5, 2024

Pricing American Options using Machine Learning Algorithms
Prudence Djagba, Callixte Ndizihiwe
Machine Learning Machine Learning Algorithm Option Pricing Optimal Bidding Strategy Black Scholes

August 26, 2024

MLP, XGBoost, KAN, TDNN, and LSTM-GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing
Boris Ter-Avanesov, Homayoon Beigi
Recurrent Neural Network XGBoost Model Multi Layer Perceptron Option Pricing Time Delay Neural Network Recursive Neural Network

May 14, 2024

Optimizing Deep Reinforcement Learning for American Put Option Hedging
Reilly Pickard, F. Wredenhagen, Y. Lawryshyn
Deep Reinforcement Learning Option Pricing Stochastic Volatility Hedging Strategy Black Scholes Option Hedging

May 9, 2024

Neural Network Learning of Black-Scholes Equation for Option Pricing
Daniel de Souza Santos, Tiago Alessandro Espinola Ferreira
Neural Network Option Pricing Black Scholes Option Pricing Model Option Forecasting

March 1, 2024

A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
Antonis Papapantoleon, Jasper Rou
Diffusion Model Option Pricing Time Step Rough Volatility

February 27, 2024

Time series generation for option pricing on quantum computers using tensor network
Nozomu Kobayashi, Yoshiyuki Suimon, Koichi Miyamoto
Quantum Computing Quantum Computer Time Series Generation Option Pricing Matrix Product State Path Dependent Option

January 12, 2024

A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models
Emmanuil H. Georgoulis, Antonis Papapantoleon, Costas Smaragdakis
Residual Neural Network Gradient Approximation Option Pricing Quadrature Rule Jump Diffusion

December 10, 2023

Physics Informed Neural Network for Option Pricing
Ashish Dhiman, Yibei Hu
Physic Informed Neural Network Physic Informed Deep Learning PINN Method Option Pricing Black Scholes

November 13, 2023

Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study
Rawin Assabumrungrat, Kentaro Minami, Masanori Hirano
Empirical Study Neural PDE Solver Error Analysis Option Pricing

October 19, 2023

American Option Pricing using Self-Attention GRU and Shapley Value Interpretation
Yanhui Shen
Long Short Term Memory Shapley Value Option Pricing

October 6, 2023

Applying Reinforcement Learning to Option Pricing and Hedging
Zoran Stoiljkovic
Reinforcement Learning Reinforcement Learning Approach Option Pricing Hedging Strategy Black Scholes

September 8, 2023

Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing
Xuwei Yang, Anastasis Kratsios, Florian Krach, Matheus Grasselli, Aurelien Lucchi
Financial Application Kernel Regression Option Pricing Federated Transfer Learning Training Pair Regret Optimal Algorithm

August 8, 2023

Efficient option pricing with unary-based photonic computing chip and generative adversarial learning
Hui Zhang, Lingxiao Wan, Sergi Ramos-Calderer, Yuancheng Zhan, Wai-Keong Mok, Hong Cai, Feng Gao, Xianshu Luo, Guo-Qiang Lo, Leong Chuan Kwek, José Ignacio Latorre, Ai Qun Liu
Generative Adversarial Amplitude Estimation Option Pricing Photonic Computing Classical Computing Photonic Chip Quadratic Speedup Photonic Processor

July 14, 2023

Machine learning for option pricing: an empirical investigation of network architectures
Laurens Van Mieghem, Antonis Papapantoleon, Jonas Papazoglou-Hennig
Machine Learning New Machine Empirical Study Machine Learning Algorithm Neural Network Architecture Network Architecture Option Pricing Supervised Learning Problem

July 2, 2023

Pricing European Options with Google AutoML, TensorFlow, and XGBoost
Juan Esteban Berger
Neural Network Machine Learning Tensorflow Probability Option Pricing AutoML Framework Black Scholes

December 28, 2022

Quantum-Inspired Tensor Neural Networks for Option Pricing
Raj G. Patel, Chia-Wei Hsing, Serkan Sahin, Samuel Palmer, Saeed S. Jahromi, Shivam Sharma, Tomas Dominguez, Kris Tziritas, Christophe Michel, Vincent Porte, Mustafa Abid, Stephane Aubert, Pierre Castellani, Samuel Mugel, Roman Orus
Deep Learning High Dimensional Partial Differential Equation Classical Neural Network Option Pricing

September 30, 2022

Multi-Task Option Learning and Discovery for Stochastic Path Planning
Naman Shah, Siddharth Srivastava
Scientific Discovery Root to Leaf Path Option Pricing Stochastic Simulation Option Learning

August 15, 2022

Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing
Danial Saef, Yuanrong Wang, Tomaso Aste
Time Series Clustering Option Pricing Stochastic Volatility Option Pricing Model

Option Pricing

Papers

Diffusion Meets Options: Hierarchical Generative Skill Composition for Temporally-Extended Tasks

Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity

Pricing American Options using Machine Learning Algorithms

MLP, XGBoost, KAN, TDNN, and LSTM-GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing

Optimizing Deep Reinforcement Learning for American Put Option Hedging

Neural Network Learning of Black-Scholes Equation for Option Pricing

A time-stepping deep gradient flow method for option pricing in (rough) diffusion models

Time series generation for option pricing on quantum computers using tensor network

A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models

Physics Informed Neural Network for Option Pricing

Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study

American Option Pricing using Self-Attention GRU and Shapley Value Interpretation

Applying Reinforcement Learning to Option Pricing and Hedging

Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing

Efficient option pricing with unary-based photonic computing chip and generative adversarial learning

Machine learning for option pricing: an empirical investigation of network architectures

Pricing European Options with Google AutoML, TensorFlow, and XGBoost

Quantum-Inspired Tensor Neural Networks for Option Pricing

Multi-Task Option Learning and Discovery for Stochastic Path Planning

Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing