Image de couverture de livre

Approximating intractable short ratemodel distribution with neural network

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Knezevic, Anna, Dokuchaev, Nikolai
Format:	Preprint
Publié:	2019
Sujets:	Machine Learning Mathematical Finance
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Enhancing path-integral approximation for non-linear diffusion with neural network
par: Knezevic, Anna
Publié: (2024)

Dynamic graph neural networks for enhanced volatility prediction in financial markets
par: Kumar, Pulikandala Nithish, et autres
Publié: (2024)

Comparative analysis of neural network architectures for short-term FOREX forecasting
par: Zafeiriou, Theodoros, et autres
Publié: (2024)

Detecting data-driven robust statistical arbitrage strategies with deep neural networks
par: Neufeld, Ariel, et autres
Publié: (2022)

Neural networks can detect model-free static arbitrage strategies
par: Neufeld, Ariel, et autres
Publié: (2023)

Universal approximation with signatures of non-geometric rough paths
par: Ceylan, Mihriban, et autres
Publié: (2026)

Transfer Learning Across Fixed-Income Product Classes
par: Camenzind, Nicolas, et autres
Publié: (2025)

Leave-one-out least squares Monte Carlo algorithm for pricing Bermudan options
par: Woo, Jeechul, et autres
Publié: (2018)

Mathematics of Differential Machine Learning in Derivative Pricing and Hedging
par: Gomes, Pedro Duarte
Publié: (2024)

Generative Path-Law Jump-Diffusion: Sequential MMD-Gradient Flows and Generalisation Bounds in Marcus-Signature RKHS
par: Bloch, Daniel
Publié: (2026)

Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure
par: Chen, Minshuo, et autres
Publié: (2025)

Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering
par: Luan, Qinmeng, et autres
Publié: (2023)

Prediction Markets as Bayesian Inverse Problems: Uncertainty Quantification, Identifiability, and Information Gain from Price-Volume Histories under Latent Types
par: Madrigal-Cianci, Juan Pablo, et autres
Publié: (2026)

Statistical Arbitrage in Rank Space
par: Li, Y. -F., et autres
Publié: (2024)

Distributional Reinforcement Learning on Path-dependent Options
par: Özsoy, Ahmet Umur
Publié: (2025)

A Topological Approach to Parameterizing Deep Hedging Networks
par: Das, Alok, et autres
Publié: (2025)

Exploratory Utility Maximization Problem with Tsallis Entropy
par: Ziyi, Chen, et autres
Publié: (2025)

Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization
par: Rovirosa, Jan, et autres
Publié: (2026)

Universal randomised signatures for generative time series modelling
par: Biagini, Francesca, et autres
Publié: (2024)

COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learning
par: Wang, Zian, et autres
Publié: (2024)

Distributionally robust risk evaluation with a causality constraint and structural information
par: Han, Bingyan
Publié: (2022)

Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework
par: Özsoy, Ahmet Umur
Publié: (2025)

A Deep Learning-Based Method for Fully Coupled Non-Markovian FBSDEs with Applications
par: Molla, Hasib Uddin, et autres
Publié: (2025)

Rough Path Signatures: Learning Neural RDEs for Portfolio Optimization
par: Alzahrani, Ali Atiah
Publié: (2025)

SigMA: Path Signatures and Multi-head Attention for Learning Parameters in fBm-driven SDEs
par: Wu, Xianglin, et autres
Publié: (2025)

Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage
par: Luo, Fusheng, et autres
Publié: (2026)

Inspectable Neural Markov Models for Non-Stationary Time Series
par: Rovirosa, Jan, et autres
Publié: (2026)

Scalable Signature-Based Distribution Regression via Reference Sets
par: Alden, Andrew, et autres
Publié: (2024)

Multi-Agent Reinforcement Learning for Greenhouse Gas Offset Credit Markets
par: Welsh, Liam, et autres
Publié: (2025)

Chaotic Hedging with Iterated Integrals and Neural Networks
par: Neufeld, Ariel, et autres
Publié: (2022)

SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate
par: Wei, Lifu, et autres
Publié: (2026)

Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics
par: Chen, Yuling Max, et autres
Publié: (2025)

Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
par: Buehler, Hans, et autres
Publié: (2025)

Neural Term Structure of Additive Process for Option Pricing
par: Lin, Jimin, et autres
Publié: (2024)

Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions
par: Bloch, Daniel
Publié: (2026)

Global universality via discrete-time signatures
par: Ceylan, Mihriban, et autres
Publié: (2026)

Global universal approximation with Brownian signatures
par: Ceylan, Mihriban, et autres
Publié: (2025)

Enhancing a Risk Model by Adding Transient Statistical Factors
par: Tzikas, Alexandros E., et autres
Publié: (2026)

An analysis of linear regression and neural networks approximation for the pricing of swing options
par: Yeo, Christian
Publié: (2023)

Reinforcement Learning for Jump-Diffusions, with Financial Applications
par: Gao, Xuefeng, et autres
Publié: (2024)