Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks
Namid R. Stillman,
Rory Baggott,
Justin Lyon,
Jianfei Zhang,
Dingqiu Zhu,
Tao Chen and
Perukrishnen Vytelingum
Papers from arXiv.org
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.
Date: 2023-11, Revised 2023-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-hme
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2311.11913
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