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Fast and Adaptive Cointegration Based Model for Forecasting High Frequency Financial Time Series

Author

Listed:
  • Paola Arce

    (Universidad Técnica Federico Santa María)

  • Jonathan Antognini

    (Universidad Técnica Federico Santa María)

  • Werner Kristjanpoller

    (Universidad Técnica Federico Santa María)

  • Luis Salinas

    (Universidad Técnica Federico Santa María
    Centro Científico Tecnológico de Valparaíso (CCTVal))

Abstract

Cointegration is a long-run property of some non-stationary time series where a linear combination of those time series is stationary. This behaviour has been studied in finance because cointegration restrictions often improve forecasting. The vector error correction model (VECM) is a well-known econometric technique that characterises short-run variations of a set of cointegrated time series incorporating long-run relationships as an error correction term. VECM has been broadly used with low frequency time series. We aimed to adapt VECM to be used in finance with high frequency stream data. Cointegration relations change in time and therefore VECM parameters must be updated when new data is available. We studied how forecasting performance is affected when VECM parameters and the length of historical data used change in time. We observed that the number of cointegration relationships varies with the length of historical data used. Moreover, parameters that increased these relationships in time led to better forecasting performance. Our proposal, called an Adaptive VECM (AVECM) is to make a parameters grid search that maximises the number of cointegration relationships in the near past. To ensure the search can be executed fast enough, we used a distributed environment. The methodology was tested using four 10-s frequency time series of the Foreign Exchange market. We compared our proposal with ARIMA and the naive forecast of the random walk model. Numerical experiments showed that on average AVECM performed better than ARIMA and random walk. Additionally, AVECM significantly improved execution times with respect to its serial version.

Suggested Citation

  • Paola Arce & Jonathan Antognini & Werner Kristjanpoller & Luis Salinas, 2019. "Fast and Adaptive Cointegration Based Model for Forecasting High Frequency Financial Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 99-112, June.
    • Handle: RePEc:kap:compec:v:54:y:2019:i:1:d:10.1007_s10614-017-9691-7
    DOI: 10.1007/s10614-017-9691-7
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    Cited by:

    1. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 369-385, January.
    2. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    3. Neeraj Dhanraj Bokde & Zaher Mundher Yaseen & Gorm Bruun Andersen, 2020. "ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling," Energies, MDPI, vol. 13(10), pages 1-24, May.
    4. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.

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