The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

Abstract

This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

Volatility contagion between cryptocurrencies, gold and stock markets pre-and-during COVID-19: evidence using DCC-GARCH and cascade-correlation network

“…In such instances, the contagion predominantly originates from these variables, impacting Bitcoin. Right-pointing arrows demonstrate a positive relationship between Bitcoin and gold or stock markets, whereas left-pointing arrows indicate an inverse relationship (Elamer et al 2022;Ibrahim et al 2022). The solid black lines within the heatmaps demarcate statistically significant relationships at a confidence level of 5%.…”

Second Moment/Order Approximations by Kernel Smoothers with Application to Volatility Estimation

“…The problem of the estimation of the volatility has a particular relevance in financial time series analysis, where the volatility represents the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns. It is still an important active area of research [1][2][3][4]. Local linear regression methods (which can resemble a kernel smoother approach) have been already proposed for volatility estimation [5][6][7].…”

Return volatility of Asian stock exchanges; a GARCH DCC analysis with reference of Bitcoin and global crude oil price movement

“…A linkage exists between crude oil price movement with the digital currency market’s (Bitcoin) returns volatility as examined by notable researchers such as (Das and Kannadhasan, 2018; Mishra, 2019b; Huynh et al , 2022; Li et al , 2022; Gkillas et al , 2022; Yousaf et al , 2022; Salisu et al , 2023; Chancharat and Butda, 2021). Likewise, the association of the volatility relationship of the digital currency (Bitcoin) market to oil price movement has also been established (Ibrahim et al , 2022; Attarzadeh and Balcilar, 2022; Feng et al , 2023). In addition, it has been investigated that oil price volatility may create volatility in the stock market as supported by notable studies such as (Smyth and Narayan, 2018; Mishra, 2017; Cheema and Scrimgeour, 2019; Ivanovski and Hailemariam, 2021; Hashmi et al , 2021; Alamgir and Amin, 2021; Jiang and Liu, 2021; Managi et al , 2022 and Escribano et al , 2023).…”