mlsanityData to 14 Oct 2026

Multi-Asset Spot and Option Market Simulation

arXiv 2112.06823 ↗First posted 13 Dec 2021q-fin.CP cs.LG q-fin.MFcited by 17

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Abstract (arXiv)

We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows. We address the high-dimensionality of market observed call prices through an arbitrage-free autoencoder that approximates efficient low-dimensional representations of the prices while maintaining no static arbitrage in the reconstructed surface. Given a multi-asset universe, we leverage the conditional invertibility property of normalizing flows and introduce a scalable method to calibrate the joint distribution of a set of independent simulators while preserving the dynamics of each simulator. Empirical results highlight the goodness of the calibrated simulators and their fidelity.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 25 of its 56 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

25 of 56 references.

References · 25

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Improved Training of Wasserstein GANscited by 2,235
  4. 2016Density estimation using Real NVPcited by 2,042
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  6. 2017Masked Autoregressive Flow for Density Estimationcited by 684
  7. 2019Neural Spline Flowscited by 596
  8. 2017Latent Space Oddity: on the Curvature of Deep Generative Modelscited by 174
  9. 2018Deep Hedgingcited by 108
  10. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 108
  11. 2019Unconstrained Monotonic Neural Networkscited by 92
  12. 2021Sig-Wasserstein GANs for Time Series Generationcited by 60
13 more
  1. 2019Neural networks for option pricing and hedging: a literature reviewcited by 40
  2. 2020Modeling Continuous Stochastic Processes with Dynamic Normalizing Flowscited by 37
  3. 2019Cubic-Spline Flowscited by 35
  4. 2020A Data-driven Market Simulator for Small Data Environmentscited by 35
  5. 2020A generative adversarial network approach to calibration of local stochastic volatility modelscited by 29
  6. 2019Deep Hedging: Learning to Simulate Equity Option Marketscited by 25
  7. 2019Noise Regularization for Conditional Density Estimationcited by 23
  8. 2019Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combinationcited by 22
  9. 2021Arbitrage-free neural-SDE market modelscited by 19
  10. 2019CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networkscited by 16
  11. 2021Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measurescited by 11
  12. 2021Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoderscited by 9
  13. 2021Monte Carlo Simulation of SDEs using GANscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 5; 2024: 3; 2025: 0; 2026: 4.

17 papers.

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  1. 2022Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformercited by 22
  2. 2023Quantum Deep Hedgingcited by 22
  3. 2022Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversionscited by 13
  4. 2024Time-Causal VAE: Robust Financial Time Series Generatorcited by 8
  5. 2022Simulating financial time series using attentioncited by 4
  6. 2023Generative Machine Learning for Multivariate Equity Returnscited by 4
  7. 2024Universal randomised signatures for generative time series modellingcited by 4
  8. 2022Estimating risks of option books using neural-SDE market modelscited by 2
  9. 2022Risk-Neutral Market Simulationcited by 2
  10. 2023Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Datacited by 2
  11. 2024NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equitiescited by 2
  12. 2023Implementing Quantum Generative Adversarial Network (qGAN) and QCBM in Financecited by 1
5 more
  1. 2026Generating Financial Time Series by Matching Random Convolutional Featurescited by 1
  2. 2026Diffusion Models for Adaptive Sequential Data Generationcited by 1
  3. 2026DYSANOS Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfacescited by 1
  4. 2023Sig-Splines: universal approximation and convex calibration of time series generative modelscited by 0
  5. 2026Robust Control under Stationary Ambiguitycited by 0