Forecasting the Returns of Cryptocurrency: A Model Averaging Approach

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.

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

“…The empirical literature related to cryptocurrency volatility modelling and forecasting is abundant, with a strand of literature adopting the classical time series models, particularly the generalized autoregressive conditional heteroscedasticity (GARCH) family of models. In this literature, some studies investigated the cryptocurrency volatility modelling based on the in-sample forecasting strategy, (Balcilar et al, 2017 ; Charles & Darné 2019 ; Cheikh et al, 2020 ; Chu et al, 2017 ; Conrad et al, 2018 ; Dyhrberg, 2016 ; Huynh et al, 2020 ; Katsiampa, 2017 ; Naimy & Hayek, 2018 ; Pichl & Kaizoji, 2017 ; Gyamerah, 2019 ; Tiwari et al, 2019 , among others), and some assessed volatility forecasting based on out-of-sample strategy for a specific forecasting horizon (Bezerra & Albuquerque, 2017 ; Catani et al, 2019 ; Naimy & Hayek, 2018 ; Peng et al, 2018 ; Xiao & Sun, 2020 , among others). This corpus uses the conventional time series models like the GARCH family models, which were extended recently in light of outliers that characterize cryptocurrency markets (Aslan & Sensoy, 2020 ; Charles & Darné, 2019 ; Catani et al, 2019 ; Trucíos, 2019 , among others).…”

Cryptocurrency puzzles: a comprehensive review and re-introduction

“…Even for the same forecast model, the predictive performance can be inconsistent across the literature as the outcome is highly sensitive to the selection of predictors, parameters, sample periods and data frequencies (Fischer et al, 2019;Anghel, 2021). So far, only Catania et al (2021) and Xiao and Sun (2020) have explored the model averaging methods to mitigate the model uncertainty and instability issues.…”

“…So far, only Catania et al. (2021) and Xiao and Sun (2020) have explored the model averaging methods to mitigate the model uncertainty and instability issues.…”

Bitcoin, uncertainty and internet searches

“…Kristoufek (2015) highlighted the effect of money supply and the role of investors’ interest in the formation of BTCs. Xiao and Sun (2020) suggested that the gold prices, the Forex market, and volatility in major financial markets impact cryptocurrency returns. Georgoula et al (2015) stated that the USD/EUR exchange rate and the hash rate have a negative and positive effect on cryptocurrency prices, respectively.…”