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Window effect with Markov-switching GARCH model in cryptocurrency market

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  • Wu, Chuanzhen

Abstract

The non-stationarity of cryptocurrency is mainly attributed to structural breaks. Many studies use the rolling windows to deal with structural breaks. However the selection of windows is an open question without a systematic answer. This study investigates the window effect on in-sample coefficient estimation and out-of-sample forecasting. The results provide evidence on the stability of coefficient estimation under various window selections. However, in forecast, some specific window size shows much better accuracy of left-tail predictions in stable patterns. It provides a possibility to get better out-of-sample forecast by choosing a window from the historical data.

Suggested Citation

  • Wu, Chuanzhen, 2021. "Window effect with Markov-switching GARCH model in cryptocurrency market," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:chsofr:v:146:y:2021:i:c:s0960077921002563
    DOI: 10.1016/j.chaos.2021.110902
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    Cited by:

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    2. Luca Grilli & Domenico Santoro, 2022. "Forecasting financial time series with Boltzmann entropy through neural networks," Computational Management Science, Springer, vol. 19(4), pages 665-681, October.
    3. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).

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