Forecasting the returns of cryptocurrency: A model averaging approach

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 13 ; Year: 2020 ; Issue: 11 ; Pages: 1-15 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Asset Pricing; Trading Volume; Bond Interest Rates ; Estimation: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• Thema: cryptocurrencies ; Mallows criterion ; model averaging ; model selection ; shrinkage ; tuning parameter choice

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Xiao, Hui ; Sun, Yiguo
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2020

Weitere Informationen

• DOI: doi:10.3390/jrfm13110278
• Handle: http://hdl.handle.net/10419/239349
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ