Artikel

Forecasting the returns of cryptocurrency: A model averaging approach

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.

Language
Englisch

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

Classification
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
Subject
cryptocurrencies
Mallows criterion
model averaging
model selection
shrinkage
tuning parameter choice

Event
Geistige Schöpfung
(who)
Xiao, Hui
Sun, Yiguo
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2020

DOI
doi:10.3390/jrfm13110278
Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Xiao, Hui
  • Sun, Yiguo
  • MDPI

Time of origin

  • 2020

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