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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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    References listed on IDEAS

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    3. Tapia, Sebastian & Kristjanpoller, Werner, 2022. "Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    4. Bruno Gav{s}perov & Marko {DJ}urasevi'c & Domagoj Jakobovic, 2024. "Finding Near-Optimal Portfolios With Quality-Diversity," Papers 2402.16118, arXiv.org.

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