Learning to simulate sequentially generated data via neural networks and Wasserstein training

DOI10.1145/3583070zbMATH Open1544.62294MaRDI QIDQ6599355

Haoyu Liu, Tingyu Zhu, Zeyu Zheng

Publication date: 6 September 2024

Published in: ACM Transactions on Modeling and Computer Simulation (Search for Journal in Brave)

zbMATH Keywords

statistical propertiesneural networksequential simulatorWasserstein training

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Probabilistic models, generic numerical methods in probability and statistics (65C20) Sequential statistical analysis (62L10)

Cites Work

• Numerical solution of stochastic differential equations with jumps in finance
• Stochastic Volatility: Origins and Overview
• DRAWDOWN MEASURE IN PORTFOLIO OPTIMIZATION
• Deep Neural Network Approximation Theory
• Quant GANs: deep generation of financial time series
• A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
• Multivariate Stochastic Volatility: A Review
• Nonparametric regression on low-dimensional manifolds using deep ReLU networks: function approximation and statistical recovery
• Title not available (Why is that?)

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