Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
173 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks (2311.11913v2)

Published 20 Nov 2023 in cs.LG, q-fin.CP, and stat.ML

Abstract: The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic outlook. In recent years, agent-based models have been developed that reproduce many features of an exchange, as summarised by a set of stylised facts and statistics. However, the ability to calibrate simulators to a specific period of trading remains an open challenge. In this work, we develop a novel approach to the calibration of market simulators by leveraging recent advances in deep learning, specifically using neural density estimators and embedding networks. We demonstrate that our approach is able to correctly identify high probability parameter sets, both when applied to synthetic and historical data, and without reliance on manually selected or weighted ensembles of stylised facts.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. ABIDES-gym: gym environments for multi-agent discrete event simulation and application to financial markets. In Proceedings of the Second ACM International Conference on AI in Finance. 1–9.
  2. Robert L Axtell and J Doyne Farmer. 2022. Agent-based modeling in economics and finance: past, present, and future. Journal of Economic Literature (2022).
  3. Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization. In Proceedings of the Third ACM International Conference on AI in Finance. 437–445.
  4. Jean-Philippe Bouchaud and Marc Potters. 2001. More stylized facts of financial markets: leverage effect and downside correlations. Physica A: Statistical Mechanics and its Applications 299, 1-2 (2001), 60–70.
  5. Johann Brehmer. 2021. Simulation-based inference in particle physics. Nature Reviews Physics 3, 5 (2021), 305–305.
  6. A data-driven market simulator for small data environments. arXiv preprint arXiv:2006.14498 (2020).
  7. Abides: Towards high-fidelity market simulation for ai research. arXiv preprint arXiv:1904.12066 (2019).
  8. Investigating the Impact of Model Misspecification in Neural Simulation-based Inference. arXiv:2209.01845 [stat.ML]
  9. Qinkai Chen and Christian-Yann Robert. 2022. Multivariate Realized Volatility Forecasting with Graph Neural Network. In Proceedings of the Third ACM International Conference on AI in Finance. 156–164.
  10. Carl Chiarella. 1992. The dynamics of speculative behaviour. Annals of operations research 37, 1 (1992), 101–123.
  11. Black-box model risk in finance. arXiv preprint arXiv:2102.04757 (2021).
  12. Towards realistic market simulations: a generative adversarial networks approach. In Proceedings of the Second ACM International Conference on AI in Finance. 1–9.
  13. Rama Cont. 2001. Empirical properties of asset returns: stylized facts and statistical issues. Quantitative finance 1, 2 (2001), 223.
  14. The frontier of simulation-based inference. Proceedings of the National Academy of Sciences 117, 48 (2020), 30055–30062.
  15. Nicols Cruz and Javier Ruiz-del Solar. 2020. Closing the simulation-to-reality gap using generative neural networks: Training object detectors for soccer robotics in simulation as a case study. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8.
  16. Neural spline flows. Advances in neural information processing systems 32 (2019).
  17. Black-box Bayesian inference for economic agent-based models. arXiv preprint arXiv:2202.00625 (2022).
  18. Calibrating agent-based models to microdata with graph neural networks. arXiv preprint arXiv:2206.07570 (2022).
  19. The microstructure of the “flash crash”: flow toxicity, liquidity crashes, and the probability of informed trading. The Journal of Portfolio Management 37, 2 (2011), 118–128.
  20. Validation of agent-based models in economics and finance. Computer simulation validation: fundamental concepts, methodological frameworks, and philosophical perspectives (2019), 763–787.
  21. Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model. arXiv preprint arXiv:2208.14207 (2022).
  22. Training deep neural density estimators to identify mechanistic models of neural dynamics. Elife 9 (2020), e56261.
  23. Limit order books. Quantitative Finance 13, 11 (2013), 1709–1742.
  24. Automatic posterior transformation for likelihood-free inference. In International Conference on Machine Learning. PMLR, 2404–2414.
  25. Evaluating and explaining generative adversarial networks for continual learning under concept drift. In 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 295–303.
  26. Ganspace: Discovering interpretable gan controls. Advances in Neural Information Processing Systems 33 (2020), 9841–9850.
  27. Likelihood-free mcmc with amortized approximate ratio estimators. In International conference on machine learning. PMLR, 4239–4248.
  28. Learning Robust Statistics for Simulation-based Inference under Model Misspecification. arXiv:2305.15871 [stat.ML]
  29. Normalizing flows: An introduction and review of current methods. IEEE transactions on pattern analysis and machine intelligence 43, 11 (2020), 3964–3979.
  30. Co-existence of trend and value in financial markets: Estimating an extended Chiarella model. Journal of Economic Dynamics and Control 112 (2020), 103791.
  31. George Papamakarios. 2019. Neural density estimation and likelihood-free inference. arXiv preprint arXiv:1910.13233 (2019).
  32. George Papamakarios and Iain Murray. 2016. Fast ε𝜀\varepsilonitalic_ε-free inference of simulation models with bayesian conditional density estimation. Advances in neural information processing systems 29 (2016).
  33. Masked autoregressive flow for density estimation. Advances in neural information processing systems 30 (2017).
  34. Econophysics: financial time series from a statistical physics point of view. Physica A: Statistical Mechanics and its Applications 279, 1-4 (2000), 443–456.
  35. Multi-agent-based order book model of financial markets. Europhysics Letters 75, 3 (2006), 510.
  36. Agent-based models of financial markets. Reports on Progress in Physics 70, 3 (2007), 409.
  37. Zijian Shi and John Cartlidge. 2023. Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology. arXiv preprint arXiv:2303.00080 (2023).
  38. George J Stigler. 1963. Public regulation of the securities markets. Bus. Law. 19 (1963), 721.
  39. Graph-informed simulation-based inference for models of active matter. arXiv preprint arXiv:2304.06806 (2023).
  40. Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators. arXiv preprint arXiv:2108.00664 (2021).
  41. Timo Teräsvirta and Zhenfang Zhao. 2011. Stylized facts of return series, robust estimates and three popular models of volatility. Applied financial economics 21, 1-2 (2011), 67–94.
  42. Get real: Realism metrics for robust limit order book market simulations. In Proceedings of the First ACM International Conference on AI in Finance. 1–8.
  43. Deeplob: Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing 67, 11 (2019), 3001–3012.
  44. Towards closing the sim-to-real gap in collaborative multi-robot deep reinforcement learning. In 2020 5th International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 7–12.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now