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Forecasting high-frequency financial time series: an adaptive learning approach with the order book data

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  • Parley Ruogu Yang

Abstract

This paper proposes a forecast-centric adaptive learning model that engages with the past studies on the order book and high-frequency data, with applications to hypothesis testing. In line with the past literature, we produce brackets of summaries of statistics from the high-frequency bid and ask data in the CSI 300 Index Futures market and aim to forecast the one-step-ahead prices. Traditional time series issues, e.g. ARIMA order selection, stationarity, together with potential financial applications are covered in the exploratory data analysis, which pave paths to the adaptive learning model. By designing and running the learning model, we found it to perform well compared to the top fixed models, and some could improve the forecasting accuracy by being more stable and resilient to non-stationarity. Applications to hypothesis testing are shown with a rolling window, and further potential applications to finance and statistics are outlined.

Suggested Citation

  • Parley Ruogu Yang, 2021. "Forecasting high-frequency financial time series: an adaptive learning approach with the order book data," Papers 2103.00264, arXiv.org.
    • Handle: RePEc:arx:papers:2103.00264
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    References listed on IDEAS

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    1. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    2. Chiranjit Chakraborty & Andreas Joseph, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    3. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
    4. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
    5. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
    6. Marco Avellaneda & Josh Reed & Sasha Stoikov, 2011. "Forecasting prices from level-I quotes in the presence of hidden liquidity," Algorithmic Finance, IOS Press, vol. 1(1), pages 35-43.
    7. Andres, P. & Harvey, A., 2012. "The Dyanamic Location/Scale Model: with applications to intra-day financial data," Cambridge Working Papers in Economics 1240, Faculty of Economics, University of Cambridge.
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    Cited by:

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    2. Marc Wildi & Branka Hadji Misheva, 2022. "A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection," Papers 2212.02906, arXiv.org.

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