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Hawkes-based cryptocurrency forecasting via Limit Order Book data

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  • Raffaele Giuseppe Cestari
  • Filippo Barchi
  • Riccardo Busetto
  • Daniele Marazzina
  • Simone Formentin

Abstract

Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series. The task becomes even more arduous when applied to cryptocurrency returns, given the chaotic and intricately complex nature of crypto markets. In this study, we present a novel prediction algorithm using limit order book (LOB) data rooted in the Hawkes model, a category of point processes. Coupled with a continuous output error (COE) model, our approach offers a precise forecast of return signs by leveraging predictions of future financial interactions. Capitalizing on the non-uniformly sampled structure of the original time series, our strategy surpasses benchmark models in both prediction accuracy and cumulative profit when implemented in a trading environment. The efficacy of our approach is validated through Monte Carlo simulations across 50 scenarios. The research draws on LOB measurements from a centralized cryptocurrency exchange where the stablecoin Tether is exchanged against the U.S. dollar.

Suggested Citation

  • Raffaele Giuseppe Cestari & Filippo Barchi & Riccardo Busetto & Daniele Marazzina & Simone Formentin, 2023. "Hawkes-based cryptocurrency forecasting via Limit Order Book data," Papers 2312.16190, arXiv.org.
    • Handle: RePEc:arx:papers:2312.16190
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    References listed on IDEAS

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    1. Charles Cao & Oliver Hansch & Xiaoxin Wang, 2009. "The information content of an open limit‐order book," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(1), pages 16-41, January.
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    4. Emilio Barucci & Giancarlo Giuffra Moncayo & Daniele Marazzina, 2022. "Cryptocurrencies and stablecoins: a high-frequency analysis," Digital Finance, Springer, vol. 4(2), pages 217-239, September.
    5. Maxime Morariu-Patrichi & Mikko S. Pakkanen, 2022. "State-dependent Hawkes processes and their application to limit order book modelling," Quantitative Finance, Taylor & Francis Journals, vol. 22(3), pages 563-583, March.
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