Revolutionizing Market Simulation: An Unbiased Approach Using Neural Density Estimators

Table of Links

Abstract and 1. Introduction

2. Relevant Work

3. Methods

3.1 Models

3.2 Summarising Features

3.3 Calibration of Market Model Parameters

4. Experiments

4.1 Zero Intelligence Trader

4.2 Extended Chiarella

4.3 Historical Data

5. Discussion & Future Work

6. Significance, Acknowledgments, and References

6 SIGNIFICANCE

In this work, we demonstrate that neural density estimators can robustly infer the parameters for a market simulator, based on two distinct theoretical models, a zero intelligence trader, and the extended Chiarella model. We combine neural density estimators to calibrate these models without using stylised facts at calibration time, a significant departure from previous methods for calibration of market simulators. Instead, we use the market simulation data directly, such as the mid-price and total volume, providing an unbiased approach to calibration that can efficiently scale. We identify interesting features of both models from our calibration procedure due to the explicit posterior probability distribution that is calculated around each parameter value, and identify future extensions and frameworks where our work can be used.

ACKNOWLEDGMENTS

We would like to thank the NVIDIA LaunchPad Experience for their help with providing compute resources for this research, especially David Taubenheim, Rafah El-Khatib, Alvin Clark and Jochen Papenbrock.

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:::info Authors:

(1) Namid R. Stillman, Simudyne Limited, United Kingdom (namid@simudyne.com);

(2) Rory Baggott, Simudyne Limited, United Kingdom (rory@simudyne.com);

(3) Justin Lyon, Simudyne Limited, United Kingdom (justin@simudyne.com);

(4) Jianfei Zhang, Hong Kong Exchanges and Clearing Limited, Hong Kong (jianfeizhang@hkex.com.hk);

(5) Dingqiu Zhu, Hong Kong Exchanges and Clearing Limited, Hong Kong (dingqiuzhu@hkex.com.hk);

(6) Tao Chen, Hong Kong Exchanges and Clearing Limited, Hong Kong (taochen@hkex.com.hk);

(7) Perukrishnen Vytelingum, Simudyne Limited, United Kingdom (krishnen@simudyne.com).

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:::info This paper is available on arxiv under CC BY 4.0 DEED license.

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