Forecasting the Returns of Cryptocurrency: A Model Averaging Approach

Hui Xiao and Yiguo Sun
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Hui Xiao: Department of Economics, Saint Mary’s University, Halifax, NS B3H 3C3, Canada

JRFM, 2020, vol. 13, issue 11, 1-15

Abstract: This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies’ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive distributed lag (ARDL) model using several popular penalized least squares estimators to explain the cryptocurrencies’ returns. We further introduce a novel model averaging approach or the shrinkage Mallows model averaging (SMMA) estimator for forecasting. First, we find that the returns for most cryptocurrencies are sensitive to volatilities from major financial markets. The returns are also prone to the changes in gold prices and the Forex market’s current and lagged information. Then, when forecasting cryptocurrencies’ returns, we further find that an ARDL( p , q ) model estimated by the SMMA estimator outperforms the competing estimators and models out-of-sample.

Keywords: cryptocurrencies; Mallows criterion; model averaging; model selection; shrinkage; tuning parameter choice (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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