Finance
Regional COVID-19 cases and Bitcoin volatility: Assessment through the Markov switching prism
Name and surname of author:
Dat Minh Phan, Sinh Duc Hoang, Tung Duy Dao, Tien Phat Pham
Keywords:
GARCH model, Chi-squared test, Bitcoin volatility modes, Pearson correlation method, statics and dynamics analysis
DOI (& full text):
Anotation:
The 21st century has become the century of technology, which has spread to the currency market, presenting the international economic system with a new challenge – the challenge created by digital currency, which has determined a change in the rules of operation in the market. The main property of cryptocurrencies in general, and Bitcoin in particular, is constant volatility and mutual sensitivity to each other. This article aims to analyze the cryptocurrency market landscape from both short-term and long-term perspectives. Additionally, the article seeks to quantitatively assess the contradictions, trends, and patterns of price volatility in Bitcoin by employing the framework of Markov switching during the period spanning from 2020 to 2022. The Markov switching model was used in the study. In this study, the factors influencing volatility on different modes of the Markov switch are the COVID-19 pandemic and the Pearson correlation statistical method. The Chisquared test was estimated to identify the connection between Bitcoin volatility switching modes and the COVID-19 pandemic spread. This analysis enables international investors to diversify with maximum efficiency and returns using available hedging tools. However, several open questions remain for future research. Future studies can analyze different cryptocurrencies’ volatility. This research helps to assess the nature of the relationship of cryptocurrencies in statistics (the correlation of cryptocurrencies as of December 1, 2021, when no significant events in the financial market and political upheavals were recorded) and dynamics (the Markov switching models for the postpandemic period of 2020–2022). The article contributes to understanding the interdependence and sensitivity of different cryptocurrencies in relation to each other.
The 21st century has become the century of technology, which has spread to the currency market, presenting the international economic system with a new challenge – the challenge created by digital currency, which has determined a change in the rules of operation in the market. The main property of cryptocurrencies in general, and Bitcoin in particular, is constant volatility and mutual sensitivity to each other. This article aims to analyze the cryptocurrency market landscape from both short-term and long-term perspectives. Additionally, the article seeks to quantitatively assess the contradictions, trends, and patterns of price volatility in Bitcoin by employing the framework of Markov switching during the period spanning from 2020 to 2022. The Markov switching model was used in the study. In this study, the factors influencing volatility on different modes of the Markov switch are the COVID-19 pandemic and the Pearson correlation statistical method. The Chisquared test was estimated to identify the connection between Bitcoin volatility switching modes and the COVID-19 pandemic spread. This analysis enables international investors to diversify with maximum efficiency and returns using available hedging tools. However, several open questions remain for future research. Future studies can analyze different cryptocurrencies’ volatility. This research helps to assess the nature of the relationship of cryptocurrencies in statistics (the correlation of cryptocurrencies as of December 1, 2021, when no significant events in the financial market and political upheavals were recorded) and dynamics (the Markov switching models for the postpandemic period of 2020–2022). The article contributes to understanding the interdependence and sensitivity of different cryptocurrencies in relation to each other.
APA Style Citation:
Phan, D. M., Hoang, S. D., Dao, T. D., & Pham, T. P. (2024). Regional COVID-19 cases and Bitcoin volatility: Assessment through the Markov switching prism. E&M Economics and Management, 27(2), 142–161. https://doi.org/10.15240/tul/001/2024-2-009