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Article Open Access November 10, 2022

Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models

Haruna Umar Yahaya 1, John Sunday Oyinloye 1 and Samuel Olorunfemi Adams 1,*
1
Department of Statistics, University of Abuja, Abuja, Nigeria
Publihed in: Universal Journal of Stomatology (Volume 2, Issue 1, 2022)
Page(s): 71-90
DOI: 10.31586/ujfe.2022.497
Received
August 01, 2022
Revised
October 31, 2022
Accepted
November 08, 2022
Published
November 10, 2022
Keywords
ARCH; Bitcoin; Crypto-currency; GARCH Model; Returns; Volatility
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2022. Published by Scientific Publications
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Cite This Article

APA Style
Yahaya, H. U. , Oyinloye, J. S. , & Adams, S. O. (2022). Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models. Universal Journal of Stomatology, 2(1), 71-90. https://doi.org/10.31586/ujfe.2022.497
ACS Style
Yahaya, H. U. ; Oyinloye, J. S. ; Adams, S. O. Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models. Universal Journal of Stomatology 2022 2(1), 71-90. https://doi.org/10.31586/ujfe.2022.497
Chicago/Turabian Style
Yahaya, Haruna Umar, John Sunday Oyinloye, and Samuel Olorunfemi Adams. 2022. "Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models". Universal Journal of Stomatology 2, no. 1: 71-90. https://doi.org/10.31586/ujfe.2022.497
AMA Style
Yahaya HU, Oyinloye JS, Adams SO. Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models. Universal Journal of Stomatology. 2022; 2(1):71-90. https://doi.org/10.31586/ujfe.2022.497 
@Article{ujs497,
AUTHOR = {Yahaya, Haruna Umar and Oyinloye, John Sunday and Adams, Samuel Olorunfemi},
TITLE = {Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models},
JOURNAL = {Universal Journal of Stomatology},
VOLUME = {2},
YEAR = {2022},
NUMBER = {1},
PAGES = {71-90},
URL = {https://www.scipublications.com/journal/index.php/UJFE/article/view/497},
ISSN = {ISSN Pending},
DOI = {10.31586/ujfe.2022.497},
ABSTRACT = {The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criterion tests. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.},
}
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%A Yahaya, Haruna Umar
%A Oyinloye, John Sunday
%A Adams, Samuel Olorunfemi
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%T Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models
%M doi:10.31586/ujfe.2022.497
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T2  - Universal Journal of Stomatology
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AB  - The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criterion tests. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.
DO  - Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models
TI  - 10.31586/ujfe.2022.497
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