Examining the return dynamics of Bitcoin and Ethereum around major international football tournaments

The purpose of this article is to analyze the effect that halving has on the fair market value of bitcoins. The main hypothesis of the study is that the decline in the cost of miners’ remuneration for mining is a significant factor that affects the price of cryptocurrencies. The article examines the factors that regulate the issuing process. The significance of a limited supply of bitcoin is detailed in the article, as well as the mechanism for the implementation of the issue of new bitcoins. The study compares the historical inflation data of the US dollar and the projected data on the inflation of bitcoin. The article analyzes the main technical element of cryptocurrency – halving – when the miner’s reward is halved. This analysis includes the mathematical methods of statistical data processing. Research results show that reducing remuneration by half every four years leads to an increased market value of the cryptocurrency. This relationship is clearly illustrated by the Kendall rank correlation method. The results of the study can have a significant impact on the fundamental assessment of bitcoin and can also enable investors to assess any of the existing and operating cryptocurrencies according to this method.

The emergence of Bitcoin in 2009 has received considerable attention surrounding the validity of cryptocurrencies as a viable and, in some jurisdictions, a legal currency alternative. Despite widespread concern that these cryptocurrencies are fostering the environment within which a substantial bubble can occur, it is important to analyze whether these new assets are behaving similarly to major international currencies. This paper investigates the effects of international monetary policy changes on bitcoin returns using a GARCH (1.1) estimation model. The results indicate that monetary policy decisions based on interest rates taken by the Federal Open Market Committee in the United States significantly impact upon bitcoin returns. After controlling for international effects, we find significant evidence of volatility effects driven by United States, European Union, United Kingdom and Japanese quantitative easing announcements. These results show that, despite its nature and ideals, bitcoin seems to be subject to the same economic factors as traditional fiat currencies, and is not entirely unaffected by government policies. This result has implications for investors using bitcoin as a hedging or diversification tool. In addition, we contribute to the existing debate regarding the classification of bitcoin as an asset class, by illustrating that bitcoin volatility exhibits various reactions that bear resemblance to both currency pairs and store-of-value assets.

This study aims to determine the impact of macroeconomic variables on bitcoin prices in the United States. Bitcoin is one of the cryptocurrencies that has the highest price and the most users in the United States in recent years. This study uses monthly data on inflation, interest rates, USD/EUR rates, gold prices, and bitcoin prices. To achieve the objectives of this study, Dynamic Conditional Correlation (DCC) and Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) were used. The results showed that there is a negative and significant relationship between the variables of inflation, interest rates, and USD/EUR rates affecting the price of Bitcoin in that period. Conversely, there is a positive and significant relationship between the price of gold and the price of Bitcoin in the United States during that period. An in-depth understanding of how macroeconomic factors such as inflation, interest rates and the USD/EUR rates affect Bitcoin price is key to making smart investment decisions in an increasingly complex crypto market. The findings of this analysis confirm that the significant relationship between macroeconomic variables and Bitcoin price provides deeper insights for investors to anticipate market movements and design adaptive investment strategies.