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Regime Change And Trend Prediction For Bitcoin Time Series Data

Author

Listed:
  • Osamu Kodama

    (International Christian University, Mitaka, Tokyo, Japan)

  • Lukáš Pichl

    (International Christian University, Mitaka, Tokyo, Japan)

  • Taisei Kaizoji

    (International Christian University, Mitaka, Tokyo, Japan)

Abstract

Bitcoin time series dataset recording individual transactions denominated in Euro at the COINBASE market between April 23, 2015 and August 15, 2016 is analyzed. Markov switching model is applied to classify the regions of varying volatility represented by three hidden state regimes using univariate autoregressive model and dependent mixture model. Causality extraction and price prediction of daily BTCEUR exchange rates is performed by means of a recurrent neural network using the standard Elman model. Strong correlations is found between the normalized mean squared error of the Elman network (out-of-sample 5-day-ahead prediction) and the realized volatility (sum of minute returns squared throughout the trading day). The present approach is calibrated using simulated regime change in standard econometric models. Our results clearly demonstrate the applicability of recurrent neural networks to causality extraction even in the case of highly volatile cryptocurrency exchange rate time series data.

Suggested Citation

  • Osamu Kodama & Lukáš Pichl & Taisei Kaizoji, 2017. "Regime Change And Trend Prediction For Bitcoin Time Series Data," CBU International Conference Proceedings, ISE Research Institute, vol. 5(0), pages 384-388, September.
    • Handle: RePEc:aad:iseicj:v:5:y:2017:i:0:p:384-388
    DOI: 10.12955/cbup.v5.954
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    Cited by:

    1. Dag, Ali & Dag, Asli Z. & Asilkalkan, Abdullah & Simsek, Serhat & Delen, Dursun, 2023. "A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends," Journal of Business Research, Elsevier, vol. 156(C).

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