Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239349 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 13 [Issue:] 11 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-15
Publisher: 
MDPI, Basel
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.
Subjects: 
cryptocurrencies
Mallows criterion
model averaging
model selection
shrinkage
tuning parameter choice
JEL: 
G12
C13
C21
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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