IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2312.16190.html
   My bibliography  Save this paper

Hawkes-based cryptocurrency forecasting via Limit Order Book data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2312.16190
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. Marcello Rambaldi & Emmanuel Bacry & Fabrizio Lillo, 2017. "The role of volume in order book dynamics: a multivariate Hawkes process analysis," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 999-1020, July.
    3. Ban Zheng & Eric Moulines & Fr'ed'eric Abergel, 2012. "Price Jump Prediction in Limit Order Book," Papers 1204.1381, arXiv.org.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shunya Chomei, 2023. "Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities," Papers 2302.01668, arXiv.org, revised Feb 2023.
    2. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods," Papers 1705.03233, arXiv.org, revised Mar 2020.
    3. Konark Jain & Nick Firoozye & Jonathan Kochems & Philip Treleaven, 2024. "Limit Order Book Simulations: A Review," Papers 2402.17359, arXiv.org, revised Mar 2024.
    4. Wu, Liang & Liu, Hengzhi & Liu, Chang & Long, Yunshen, 2020. "Determining the information share of liquidity and order flows in extreme price movements," Economic Modelling, Elsevier, vol. 93(C), pages 559-575.
    5. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2020. "A new wavelet-based ultra-high-frequency analysis of triangular currency arbitrage," Economic Modelling, Elsevier, vol. 85(C), pages 57-73.
    6. Peng Wu & Marcello Rambaldi & Jean-Franc{c}ois Muzy & Emmanuel Bacry, 2019. "Queue-reactive Hawkes models for the order flow," Papers 1901.08938, arXiv.org.
    7. Ioane Muni Toke & Nakahiro Yoshida, 2020. "Marked point processes and intensity ratios for limit order book modeling," Papers 2001.08442, arXiv.org.
    8. Ban Zheng & François Roueff & Frédéric Abergel, 2014. "Ergodicity and scaling limit of a constrained multivariate Hawkes process," Post-Print hal-00777941, HAL.
    9. Vinay Patel, 2015. "Price Discovery in US and Australian Stock and Options Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 27, July-Dece.
    10. Alexis Stenfors & Masayuki Susai, 2021. "Stealth Trading in FX Markets," Working Papers in Economics & Finance 2021-02, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    11. Mazza, Paolo, 2015. "Price dynamics and market liquidity: An intraday event study on Euronext," The Quarterly Review of Economics and Finance, Elsevier, vol. 56(C), pages 139-153.
    12. Matthew F Dixon, 2017. "A High Frequency Trade Execution Model for Supervised Learning," Papers 1710.03870, arXiv.org, revised Dec 2017.
    13. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
    14. Dat Thanh Tran & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Tensor Representation in High-Frequency Financial Data for Price Change Prediction," Papers 1709.01268, arXiv.org, revised Nov 2017.
    15. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
    16. Chen, Yuanyuan & Gao, Xuefeng & Li, Duan, 2018. "Optimal order execution using hidden orders," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 89-116.
    17. Jain, Pawan & Jiang, Christine, 2014. "Predicting future price volatility: Empirical evidence from an emerging limit order market," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 72-93.
    18. Masatoshi Goda, 2023. "Sparse estimation for generalized exponential marked Hawkes process," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 139-169, April.
    19. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015.
    20. Kyungsub Lee, 2024. "Discrete Hawkes process with flexible residual distribution and filtered historical simulation," Papers 2401.13890, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2312.16190. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.