Abstract
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.
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