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Rebuilding the limit order book: sequential Bayesian inference on hidden states

Author

Listed:
  • Hugh L. Christensen
  • Richard E. Turner
  • Simon I. Hill
  • Simon J. Godsill

Abstract

The limit order book of an exchange represents an information store of market participants' future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent out. We present an online algorithm which carries out Bayesian inference to replace information lost at the level of the exchange server and apply our proof of concept algorithm to real historical data from some of the world's most liquid futures contracts as traded on CME GLOBEX, EUREX and NYSE Liffe exchanges.

Suggested Citation

  • Hugh L. Christensen & Richard E. Turner & Simon I. Hill & Simon J. Godsill, 2013. "Rebuilding the limit order book: sequential Bayesian inference on hidden states," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1779-1799, November.
    • Handle: RePEc:taf:quantf:v:13:y:2013:i:11:p:1779-1799
    DOI: 10.1080/14697688.2013.851402
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    References listed on IDEAS

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    Cited by:

    1. Thomas Spooner & John Fearnley & Rahul Savani & Andreas Koukorinis, 2018. "Market Making via Reinforcement Learning," Papers 1804.04216, arXiv.org.
    2. Efstathios Panayi & Gareth W. Peters, 2015. "Stochastic simulation framework for the limit order book using liquidity-motivated agents," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 1-52.
    3. Efstathios Panayi & Gareth Peters, 2015. "Stochastic simulation framework for the Limit Order Book using liquidity motivated agents," Papers 1501.02447, arXiv.org, revised Jan 2015.

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