Abstract
A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e. the buyer- and the seller-initiated trading volumes and the order flow dynamics. Analysing order flow series and its information content of mini Nikkei 225 index futures traded at the Osaka Securities Exchange in 2012 and 2013, a data-driven optimal length of local windows up to approximately 1–2 h is reasonable to capture parameter variations and is suitable for short-term prediction. Our proposed trading strategies achieve statistical arbitrage opportunities and are, therefore, beneficial for quantitative finance practice.
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Source: Härdle et al. (2015) (colour figure online)
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Acknowledgements
The authors would like to thank the participants of the UTS-QMF 2014 Quantitative Methods in Finance Conference 2014, successfully co-organised by Professors Eckhard Platen (Quantitative Finance editor), Erik Schlögl and Carl Chiarella, as well as their team at the University of Technology, Sydney. We furthermore thank the editor and two anonymous reviewers for their constructive comments which helped us to improve the manuscript.
Funding
This work was supported by the Deutsche Forschungsgemeinschaft via CRC 649 ‘Economic Risk’ and IRTG 1792 ‘High Dimensional Non Stationary Time Series’, Humboldt-Universität zu Berlin.
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Mihoci, A., Ting, CA., Lu, MJ. et al. Adaptive order flow forecasting with multiplicative error models. Digit Finance 4, 89–108 (2022). https://doi.org/10.1007/s42521-021-00047-1
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DOI: https://doi.org/10.1007/s42521-021-00047-1