REGIME CHANGE AND TREND PREDICTION FOR BITCOIN TIME SERIES DATA

Osamu Kodama (), Lukáš Pichl () and Taisei Kaizoji ()
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Osamu Kodama: International Christian University, Mitaka, Tokyo, Japan
Lukáš Pichl: International Christian University, Mitaka, Tokyo, Japan

CBU International Conference Proceedings, 2017, vol. 5, issue 0, 384-388

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.

Keywords: BitcoinBTC; Elman model; Hidden Markov Model; HMM; recurrent neural network (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:aad:iseicj:v:5:y:2017:i:0:p:384-388

DOI: 10.12955/cbup.v5.954

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