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Modelling Asset Prices for Algorithmic and High-Frequency Trading

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  • �lvaro Cartea
  • Sebastian Jaimungal

Abstract

Algorithmic trading (AT) and high-frequency (HF) trading, which are responsible for over 70% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this article, we employ a hidden Markov model to examine how the intraday dynamics of the stock market have changed and how to use this information to develop trading strategies at high frequencies. In particular, we show how to employ our model to submit limit orders to profit from the bid-ask spread, and we also provide evidence of how HF traders may profit from liquidity incentives (liquidity rebates). We use data from February 2001 and February 2008 to show that while in 2001 the intraday states with the shortest average durations (waiting time between trades) were also the ones with very few trades, in 2008 the vast majority of trades took place in the states with the shortest average durations. Moreover, in 2008, the states with the shortest durations have the smallest price impact as measured by the volatility of price innovations.

Suggested Citation

  • �lvaro Cartea & Sebastian Jaimungal, 2013. "Modelling Asset Prices for Algorithmic and High-Frequency Trading," Applied Mathematical Finance, Taylor & Francis Journals, vol. 20(6), pages 512-547, December.
    • Handle: RePEc:taf:apmtfi:v:20:y:2013:i:6:p:512-547
    DOI: 10.1080/1350486X.2013.771515
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    Citations

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    Cited by:

    1. Campi, Luciano & Zabaljauregui, Diego, 2020. "Optimal market making under partial information with general intensities," LSE Research Online Documents on Economics 104612, London School of Economics and Political Science, LSE Library.
    2. Yang, Yurun & Göncü, Ahmet & Pantelous, Athanasios A., 2018. "Momentum and reversal strategies in Chinese commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 60(C), pages 177-196.
    3. Vikram Krishnamurthy & Sujay Bhatt, 2015. "Sequential Detection of Market shocks using Risk-averse Agent Based Models," Papers 1511.01965, arXiv.org.
    4. Carè, Rosella & Cumming, Douglas, 2024. "Technology and automation in financial trading: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 71(C).
    5. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 91-114, March.
    6. 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.
    7. Choi, So Eun & Jang, Hyun Jin & Lee, Kyungsub & Zheng, Harry, 2021. "Optimal market-Making strategies under synchronised order arrivals with deep neural networks," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    8. Álvaro Cartea & Sebastian Jaimungal & Damir Kinzebulatov, 2016. "Algorithmic Trading With Learning," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-30, June.
    9. Charles-Albert Lehalle & Eyal Neuman, 2019. "Incorporating signals into optimal trading," Finance and Stochastics, Springer, vol. 23(2), pages 275-311, April.
    10. Amit Bhaya & Eugenius Kaszkurewicz & Leonardo Valente Ferreira, 2024. "A Dynamic Trading Model for Use with a One Step Ahead Optimal Strategy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1575-1608, April.
    11. Xiaofei Lu & Fr'ed'eric Abergel, 2018. "Order-book modelling and market making strategies," Papers 1806.05101, arXiv.org.
    12. M. Alessandra Crisafi & Andrea Macrina, 2016. "Simultaneous Trading In ‘Lit’ And Dark Pools," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(08), pages 1-33, December.
    13. Gao, Xuefeng & Xu, Tianrun, 2022. "Order scoring, bandit learning and order cancellations," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    14. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    15. Xin Guo & Zhao Ruan & Lingjiong Zhu, 2015. "Dynamics of Order Positions and Related Queues in a Limit Order Book," Papers 1505.04810, arXiv.org, revised Oct 2015.
    16. 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.
    17. Farzad Alavi Fard, 2014. "Optimal Bid-Ask Spread in Limit-Order Books under Regime Switching Framework," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 33-48, November.
    18. Diego Zabaljauregui, 2020. "Optimal market making under partial information and numerical methods for impulse control games with applications," Papers 2009.06521, arXiv.org.
    19. Diego Zabaljauregui & Luciano Campi, 2019. "Optimal market making under partial information with general intensities," Papers 1902.01157, arXiv.org, revised Apr 2020.
    20. Colaneri, Katia & Eksi, Zehra & Frey, Rüdiger & Szölgyenyi, Michaela, 2020. "Optimal liquidation under partial information with price impact," Stochastic Processes and their Applications, Elsevier, vol. 130(4), pages 1913-1946.

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