Pages that link to "Item:Q5139243"
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The following pages link to Quant GANs: deep generation of financial time series (Q5139243):
Displaying 22 items.
- QuantGAN (Q1352400) (← links)
- Forecasting financial time series with Boltzmann entropy through neural networks (Q2109012) (← links)
- cCorrGAN: conditional correlation GAN for learning empirical conditional distributions in the elliptope (Q2117912) (← links)
- Robust utility maximization under model uncertainty via a penalization approach (Q2120592) (← links)
- A generative model for fBm with deep ReLU neural networks (Q2171942) (← links)
- Grouping of contracts in insurance using neural networks (Q5003353) (← links)
- Generative adversarial networks for financial trading strategies fine-tuning and combination (Q5014212) (← links)
- Monte Carlo simulation of SDEs using GANs (Q6072362) (← links)
- Estimation of extreme quantiles from heavy-tailed distributions with neural networks (Q6089222) (← links)
- Continuous and impulse controls differential game in finite horizon with Nash-equilibrium and application (Q6098966) (← links)
- Generative adversarial networks applied to synthetic financial scenarios generation (Q6099023) (← links)
- Empirical deep hedging (Q6101025) (← links)
- Modern AI \textit{versus} century-old mathematical models: how far can we go with generative adversarial networks to reproduce stochastic processes? (Q6111930) (← links)
- Recent advances in reinforcement learning in finance (Q6146668) (← links)
- A generative model of a limit order book using recurrent neural networks (Q6166215) (← links)
- GANs training: A game and stochastic control approach (Q6196295) (← links)
- Designing universal causal deep learning models: The geometric (Hyper)transformer (Q6196301) (← links)
- Fin-GAN: forecasting and classifying financial time series via generative adversarial networks (Q6546310) (← links)
- 30 years of synthetic data (Q6577810) (← links)
- Connecting GANs, mean-field games, and optimal transport (Q6598422) (← links)
- Learning to simulate sequentially generated data via neural networks and Wasserstein training (Q6599355) (← links)
- On the causality-preservation capabilities of generative modelling (Q6653565) (← links)
