Both cryptocurrencies and gold are scarce, expensive for extraction, and less affected by money supply. We focus on these similarities and investigate whether cryptocurrency network affects impact on expected return on gold. Our results show that the number of cryptocurrency wallet users is positively related to the expected return on gold. Moreover, we employed a machine-learning approach and considered the interactions among predictors. We reveal that network factors have a greater impact on gold than returns on Bitcoin and other macroeconomic and financial variables.
Bitcoin is the world's first cryptocurrency which has largest market capitalization. The study aims to analyze the risk measures for the bitcoin and comparing with tradable asset classes that include the Standard and Poor's BSE 500, USD, Euro, GBP and the Gold future prices. The study uses the GARCH models to identify the components of world economies that bitcoin is sensitive too as against variables that impact the global financial prudence. The empirical results of the study reveal that against dollar and euro exchange rates bitcoin returns are more sensitive. Bitcoin can be used together with gold to diversify or eliminate explicit market risks. The study presents reasonable justification over the development and relationship between bitcoin and different traded assets that pose new challenges before the global investors. The implication of this paper for the strategic policy makers shows the sensitivity among tradeable assets.
The blockchain technology and cryptocurrency are now in the centre of the financial market. The raise of the cryptocurrencies represented by Bitcoin have attracted a large group of scholars to analyze the underlying dynamics of their price fluctuations. Intensive debate emerged on the intrinsic features of Bitcoin. In theoretical analysis, we developed the principle of monetary convention to define the concept of monetary consensus, capturing the nature of monetary system, and categorize it into three types: traditional, algorithm and hybrid. Based on the Wavelet Coherence Analysis, we try to analyze Bitcoin price dynamics in both time and frequency domains, comparing Bitcoin with financial assets, economic and financial indexes, and other cryptocurrencies.
The article analyzes the impact of 44 different factors on the change in the price of bitcoin, such as: yields on US one-year bonds, prices for futures on palladium, platinum, gold, silver, copper, Chinese stock indices, German stock index DAX, French index CAC 40, stock indices of South Africa, India, Mauritius, England, Switzerland, Sweden, South Korea, etc. The most influential factors were selected, with the help of which two regression models were built. The presence of insignificant factors in the first model led to the need to consider the second one, which is much better. The second model lacks multicollinearity, but there are autocorrelation of residues and heteroscedasticity. There was an attempt to eliminate autocorrelation of residues, but it led to a significant reduction in the coefficient of determination to 22%. An attempt to explain the causality of the Bitcoin exchange rate to the KOSPRI index has been made. South Korea is a country, which actively cooperates with two main leaders in Bitcoin mining, namely China, where 65 % of new bitcoins are created, and the United States – 23 % of new bitcoins. Modern economic leaders actively influence not only the economy of their country, but also other economies. Korean companies are actively penetrating both the Chinese economy and the US national economy, which determines their impact on Bitcoin mining, as a large amount of necessary electronics is produced by South Korean companies such as SK hynix Inc. As for the palladium futures rate, palladium is a metal which is actively used in production of the electronics needed to mine bitcoin. Prospects for further research depend on trends in the price of bitcoin. It is possible that the price of the cryptocurrency Bitcoin will gradually rise, as some corporations begin to recognize Bitcoin as a means of payment for goods and services. This will increase demand and, as a result, prices. The confidence of large companies in Bitcoin will indicate the reliability of the cryptocurrency, which in turn will give confidence to minority investors in the need to add cryptocurrency to investment portfolios. The rise in prices will indicate the need to study the impact of various factors on changes in the price of Bitcoin. If the popularity of cryptocurrency begins to wane, it will lead to a rapid loss of its price. This development option will indicate the inexpediency of continuing to study the relationship of this cryptocurrency with these factors and the need to find other ones.
This article investigates similarities and differences between gold and four cryptocurrencies (Bitcoin, Ethereum, Bitcoin Cash and Litecoin). To do so, we estimate a system-GARCH-in-mean with respect to four determinants for the period starting 7/18/2014 at earliest until 7/12/2021. We find that, first, liquidity premia are less important. Second, volatility premia exist in either gold and cryptocurrencies. Third, the response of cryptocurrencies to ex- change rate changes is more pronounced than for gold at least if developing countries are included. Fourth, gold exhibits a safe haven status, while cryptocurrencies do not. So those cannot be seen as a store of value but rather as speculative assets.
Md. Jamal Hossain, Mohd Tahir Ismail, Mohammad Raquibul Hossain
This paper investigated the existence of structural break and impact of Gold and Platinum value on Bitcoin value. We also examined changes in the regime using three MSGARCH models, namely single-regime, two-regime, and three-regime, and compared their performances. We found the presence of break and significant impact of the Platinum value. Previous studies found that Gold has an impact on Bitcoin value, but empirically we could not find any. The two-regime MSGARCH model performs well among three models, and there is evidence of low volatility and high volatility regime. The out-of-sample forecast performance is almost same in the three models. In the long run, the presence of low volatility regime is more prominent than the high volatility regime.
Didik Gunawan, Mangasi Sinurat, Lukito Cahyadi, Rico Nur Ilham
This study aims to examine the dynamic relationship between the JKSE, S&P 500, gold prices, and bitcoin prices after WHO declared Covid-19 a global pandemic. The data used is daily data from March to November 2020 which follows trading days in the Indonesian capital market. Furthermore, this research uses VAR modelling to see how the impact of the Covid-19 pandemic on the relationship between the JCI, S&P 500, gold prices and bitcoin prices. The results showed that in the short term the S&P 500 has a positive and significant effect on JKSE, but in the long run it has no significant positive effect, in the long run the gold price has a negative and significant effect on JKSE and vice versa has no effect in the short term, both in the long term and in the short-term bitcoin has a negative and significant effect on JKSE. This research also shows that apart from gold, bitcoin has also become a safe haven for investors.
Adedeji Daniel Gbadebo, Ahmed Oluwatobi Adekunle, Wole Adedokun, Adebayo-Oke Abdulrauf Lukman · 5 authors
This paper offers a plausible response to “what explains the sporadic volatility in the price of Bitcoin?” We hypothesized that market “fundamentals” and “information demands” are key drivers of Bitcoin’s unpredictable price fluctuation. We adopt the transfer-function [Autoregressive Distributed Lag, ARDL] model and its Bounds testing approach to verify how the volatility of the price of Bitcoin responds to its transaction volume, cryptocurrency market capitalisation, world market equity index and Google search. We found the existence of long-run cointegration relation and observed that all the variables except the equity index positively explain the volatility of Bitcoin price. The result established evidence that market fundamentals drive erratic swing in Bitcoin price than information.
Cryptocurrency works on a system that admits people to make payments all over the world without the requirement for any intermediary. Most digital currencies experience frequent periods of intense volatility. This paper examines the day of the week effects in return and volatility on Bitcoin, Ethereum, Ripple, Litecoin, and Tether currencies. To estimate volatile variance, this research uses five ARCH family models: ARCH, GARCH, EGARCH, TARCH and PARCH Models. The best models are derived based on Akaike Info Criterion and Schwarz Criterion. The sample periods vary based on the date of the initial release of each currency up to 31 December 2019. Results indicate the Power ARCH (PARCH) is the best model for Bitcoin and Litecoin, Threshold ARCH (TARCH) model is the best for Ethereum, Ripple, and Litecoin, and the EGARCH model is for Tether. Each model shows a different day of the week effects on each currency.
The aim of this paper is to find out if the COVID-19 outbreak in the USA has a robust impact on the prices of cryptocurrencies. Inspired by the literature related to the determinants of cryptocurrency prices and based on data availability, six potential determinants of cryptocurrency prices and five proxies for the COVID-19 outbreak were selected. The impact of the COVID-19 outbreak was tested using two approaches of extreme bounds analysis and the robustness of our findings was further checked with different cryptocurrencies (Bitcoin, Ethereum, Litecoin and Bitcoin Cash). Our results show that new deaths from the COVID-19 have a robust positive impact on the price of cryptocurrencies while the impact of new confirmed cases, total cases, and total deaths is not robust. In line with previous studies, it is also found that economic uncertainty, stock, gold, and oil prices are robust determinants of the value of cryptocurrencies.
The work is devoted to identifying and tracking development trends, structural shifts in the economy under the influence of world markets, represented by non-stationary time series of gold, bitcoin and oil prices. The heuristic potential of the concept of the long and medium wave is used for forecasting purposes. The analysis of financial time series using the adaptive correlation coefficient is carried out. The dynamics of the traditional coefficient appears to be a significantly smoothed graph, which prevents sufficient qualitative analysis of the data. The results obtained are analysed to identify wave fluctuations, to determine the phases of growth, prosperity, recession and stagnation in the economy. An overview of the situation on the world markets for gold, bitcoin and oil based on the considered time series is presented. Based on the identified trends in the dynamics of these markets, short-term forecasting was carried out using ARIMA models and neural networks. The statistical calculations R environment is used.
COVID-19 pandemic has caused significant losses and an increase in the level of risk in the financial markets and global economy. Thus in this study, we model the crypto market volatility behavior during the COVID-19 crisis. GARCH (1, 1) and GJR-GARCH (1, 1) were applied to model the volatility clustering and leverage effects in the intraday day (15-minute interval) returns of Bitcoin, Ethereum, and Litcoin ranging from 11th April 2019 to 8th February 2021. The empirical findings from GARCH (1, 1) model indicates the presence of volatility clustering in the crypto market. Moreover, the results of the GJR-GARCH (1, 1) indicate the presence of leverage effects in the financial returns series of all three crypto currencies. Furthermore, the excess kurtosis confirms the existence of fat-tail phenomena in the crypto market. Overall, the findings from this study showed that in times of COVID 19 pandemic the crypto market returns series showed volatility persistence, fat-tail phenomena, and leverage effects. These outcomes provide a better understanding for financial investors to invest rationally and cautiously during pandemic times.
Today, cryptocurrencies and topics related to information technology are attracting more attention not only on the part of traders, but also scientists. More research is being carried out aimed at the thorough study of cryptocurrencies, as well as the search for ways to facilitate interaction with blockchain. The topic of data analysis for cryptocurrencies is becoming increasingly important as the number of companies dependent on cryptocurrencies is growing rapidly. There are problems related to the cryptocurrency trading process, such as forecasting prices and trends, forecasting volatility, building a portfolio, detecting fraud, analyzing indicators for various cryptocurrencies. To solve these problems, trading bots are used. Trading bots are software products or websites that offer so-called «algorithmic trading», as they automatically analyze the actions and indicators of the market, offer strategies to maximize the trader’s profits and increase his satisfaction. They can aggregate historical market data, calculate indicators, model the order fulfillment and can even be set up to execute strategies while the customer is asleep. When analyzing the needs of the market, it turned out that there was a lack of a chat bot that would help traders or simply persons interested in the topic of cryptocurrencies to receive fresh information about the latest changes in the market. The article considers the functions and examples of performance of the chat bot CryptoAlert, created by one of the authors, which helps users to always be aware of the latest changes in the cryptocurrency market. The main function of the bot is to receive notifications about significant changes in the price of the selected coin. The use of CryptoAlert facilitates the trader’s work and significantly increases the likelihood of successful trading in the market.
Sahar Loukil, Mouna Aloui, Ahmed Jeribi, Anis Jarboui
This study examines the safe haven properties of top five crypto-currencies, oil and gold for the five gulf cooperation council countries in view of COVID-19 period through a nonlinear and asymmetric framework NARDL methodology to uncover short- and long-run asymmetries. Using daily data from January 2019 to April 2020, we find that Bitcoin and Ethereum are safe haven assets for GCC in instability; Bitcoin is a safe haven for Oman, Saudi Arabia and Abu Dhabi. Ethereum is a safe haven for Bahrain, Kuwait and Qatar. Further, for Kuwait, Qatar, Saudi Arabia and Abu Dhabi, oil is a safe haven asset in mitigated period. We also notice that the strategies of hiding differ interestingly for all countries except for Saudi Arabia that does not significantly change its strategies. Thus, portfolio managers may consider few eligible crypto-currencies and oil for their inclusion into the portfolio to hedge risk. While, speculators acting in both stock and crypto market may go for a spread strategy. Our research is useful for portfolio managers and financial advisors looking for the best of crypto's, gold and oil to hedge shocks in stock market indices.