Idiosyncrasies of Money: 21st Century Evolution of Money

This paper examines the question of what kind of money will govern the 21st century by examining the developments which characterise this landscape. On the basis of a review of the available literature and evidence, it is clear that certain technological innovations, such as the movement towards electronic money, will undoubtedly change how we operate. However, the conclusion in this paper is less sanguine regarding the prospects of a global currency, regional monetary unions, or states' exit from or central banks' control of money. This paper also sees poor prospects for cryptocurrencies at the moment, given their focus on the decentralisation and politicisation of money, because money requires a backstopping force, making it inherently political. Finally, this paper considers how regulators may seek to ensure that money in its digital form is not taken advantage of and applied in malevolent activities. The study used correlation to establish the level of association among variables. A multiple regression analysis was used to draw an econometric model explaining the relationship between the independent and dependent variables. The following variables were used as independent variables: monetary aggregate (M1), harmonised index of consumer prices (HICP), Euro Interbank Offered Rate (EURIBOR), US dollar/euro, and the USD value of Bitcoin. Multiple regression predicted that when inflation rises, the money supply will decrease. M1 includes cash in circulation, current deposits, and other than demand deposits. The study concludes that price increases encourage people to keep their money in longer-term deposits, including in cryptocurrency. Additionally, an increase in EURIBOR and US dollar/euro reduces the supply of money. Otherwise, an increase in the price of bitcoin in the economy would increase the overall money supply

Modelling and Forecasting the Trend in Cryptocurrency Prices

The prediction of cryptocurrency prices is a hot topic among academics. Nevertheless, predicting the cryptocurrency price accurately can be challenging in the real world. Numerous studies have been undertaken to determine the best model for successful prediction. However, they lacked correct results because they avoided identifying the critical features. It is important to remember that trends are critical features in time series to obtain data information. A dearth of research demonstrates that the cryptocurrency trend comprises linear and nonlinear patterns. Therefore, this study attempted to fill this gap and focused on modelling and forecasting trends in cryptocurrency. This study examined the linear and nonlinear dependency trend patterns of the top five cryptocurrency closing prices. The weekly historical data of each cryptocurrency were taken at different periods due to the availability of data on the system. In achieving its goal, this study examined the results by plotting based on residual trend and diagnostic statistic checking using three deterministic methods: linear trend regression, quadratic trend, and exponential trend. Based on the minimum Akaike Information Criterion (AIC), the result showed that the top five cryptocurrency closing price data series contained nonlinear and linear trend patterns. The information of this study will assist traders and investors in comprehending the trend of the top five cryptocurrencies and choosing the suitable model to predict cryptocurrency prices. Additionally, accurately measuring the forecast will protect investors from losing their investment

Do cryptocurrency prices camouflage latent economic effects? A Bayesian hidden Markov approach

With Bitcoin, Ether and more than 2000 cryptocurrencies already forming a multi-billion dollar market, a proper understanding of their statistical and financial properties still remains elusive. Traditional economic theories do not explain their characteristics and standard financial models fail to capture their statistic and econometric attributes such as their extreme variability and heteroskedasticity. Motivated by these findings, we study Bitcoin and Ether prices via a Non-Homogeneous Pólya Gamma Hidden Markov (NHPG) model that has been shown to outperform its counterparts in conventional financial data. The NHPG algorithm has good in-sample performance and identifies both linear and non-linear effects of the predictors. Our results indicate that all price series are heteroskedastic with frequent changes between the two states of the underlying Markov process. In a somewhat unexpected result, the Bitcoin and Ether prices, although correlated, are significantly affected by different variables. We compare long term to short term Bitcoin data and find that significant covariates may change over time. Limitations of the current approach—as expressed by the large number of significant predictors and the poor out-of-sample predictions—back earlier findings that cryptocurrencies are unlike any other financial asset and hence, that their understanding requires novel tools and ideas