Dependence Structure Among Cryptocurrencies (original) (raw)
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What Coins Lead in the Cryptocurrency Market: Using Copula and Neural Networks Models
Journal of Risk and Financial Management, 2019
Exploring dependence structures between financial time series has been important within a wide range of applications. The main aim of this paper is to examine dependence relationships among five well-known cryptocurrencies—Bitcoin, Ethereum, Litecoin, Ripple, and Stella—by a copula directional dependence (CDD). By employing a neural network autoregression model to avoid the serial dependence in each individual cryptocurrency, we generate residuals of the fitted models with time series of daily log-returns in percentage of the five cryptocurrencies and then we apply a Gaussian copula marginal beta regression model to the residuals to explore the CDD. The results show that the CDD from Bitcoin to Litecoin is highest among all ordered directional dependencies and the CDDs from Ethereum to the other four cryptocurrencies are relatively higher than the CDDs to Ethereum from those cryptocurrencies. This finding implies that the return shocks of Bitcoin have the most effect on Litecoin and...
Assessing the Risk Characteristics of the Cryptocurrency Market: A GARCH-EVT-Copula Approach
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The cryptocurrency market offers significant investment opportunities but also entails higher risks as compared to other asset classes. This article aims to analyse the financial risk characteristics of individual cryptocurrencies and of a broad cryptocurrency market portfolio. We construct a portfolio comprising the 20 largest cryptocurrencies, which cover 82.1% of the total cryptocurrency market. The returns are examined for extreme tail risks by the application of Extreme Value Theory. We utilise the GARCH-EVT approach in combination with a novel algorithm to automatically determine the optimal threshold to model the tail distribution. Furthermore, we aggregate the individual market risks with a t-Student Copula to investigate possible diversification effects on a portfolio level. The empirical analysis indicates that all examined cryptocurrencies show high volatility in their price movements, whereby Bitcoin acts as the most stable cryptocurrency. All return distributions are he...
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Sretrech journals, 2018
The purpose of this study is to develop robust estimation of association between two types of crypto-currencies namely Bitcoin and Ethereum. Daily data of crypto-currencies are collected from https://coinmarketcap.com. The period for data analysis is started from January 2017 until October 2018. The value of mean return for Bitcoin is 13.18 %. Meanwhile, the value of mean return for Ethereum is 27.85 %. The standard deviation for Bitcoin is 30.27 % and Ethereum is 64.24 %. Then, this study performed Person product moment coefficient analysis to evaluate the correlation between these two crypto-currencies. Result indicates the association coefficient value is 0.50. The correlation shows there is strong positive correlation between Bitcoin return and Ethereum return. As conclusion, there is significant relationship between Bitcoin and Ethereum return data with strong positive correlation (r = 0.503, n = 21, p =0.020).The significant of this study is to help investors to make better decision in selecting appropriate investment portfolio for their investment fund that contributes better return and lower risk.
A Study of the Basic Financial and Risk Statistics of Cryptocurrencies
Bitcoin, the first cryptocurrency, was created in 2009 and ever since it has rattled the financial market. Soaring from 1,000tojustunder1,000 to just under 1,000tojustunder20,000 in 2017, Bitcoin was just the start of the cryptocurrency financial sphere piquing the interest from common man to Nobel laureates alike. With such a short history, and only gaining mainstream attention in 2017, the cryptocurrency assets have been little studied. Thus, this paper hopes to gain a better insight into the field’s risks, modeling, dependencies, and causalities by comparing a cryptocurrency to a financial equity portfolio and a portfolio of 10 cryptocurrencies. The paper is broken into three key scopes of analysis to be explored and referenced throughout as Portfolio Statistics and Risk Profile, Time Scaling, and Cryptocurrency Dependency and Causality. The first area of exploration analyzes the statistics of two financial data portfolios, presenting the opportunity to gain a preliminary comparison of FX/equity portfolios and cryptocurrencies’ risk profiles. It aims via parametric and non-parametric calculations of Value-at-Risk and Conditional Value at Risk to gain the best overall insight of each respective portfolios’ risks. The second investigation analyzes the portfolios as random walks, signals, and time series to detect deviations from these analysis assumptions and evaluate financial returns at different time scales to more accurately model each’s intrinsic statistics and create better forecasts. The last consideration then breaks from these two portfolio comparisons, analyzing various cryptocurrency dependencies and causalities to better understand the returns of these “hot” financial products.
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2020
One of the most important innovations brought by digitalization is crypto money known as virtual money. Cryptocurrencies, which have been discussed in recent years and especially a new portfolio for investors, are very popular. Bitcoin is the most well-known of these cryptographic systems, which do not depend on a central authority and have maximum reliability. The effects of various financial indicators on cryptoparas were examined in this study. The model includes a daily database in between April 3, 2018 to December 31, 2019. Initially stationarity is tested with unit root tests. Then cointegration and causality tests are employed. Impulse response is also implemented and analysed.
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This study is confined in analysing how the economic policy uncertainty (EPU) effects affect exchange rates on cryptocurrency assets in times of financial turbulence characterized by low confidence in the financial stock markets, and tranquil periods where the financial stock markets behave smoothly. Our research employs the D-Vine pair-copula method on daily selected cryptocurrency (Bitcoin, Ethereum and Ripple) prices within the period of the 10 August 2016 to the 23 February 2018. Our findings document the presence of the dependence between the US EPU and cryptocurrencies and indicate a significant correlation with Ethereum which exhibits a much better return.
The Interdependence of Bitcoin and Financial Markets: A Copula-Garch Approach
The Interdependence of Bitcoin and Financial Markets: A Copula-Garch Approach, 2020
This paper aims to examine the relationship between Bitcoin and preeminent financial indicators using Copula-GARCH method. In the study, we use closing prices of Bitcoin and US 10-Year Bond Yield, Gold Spot US Dollar, US Dollar Index, S&P 500, FTSE 100 and NIKKEI 225. To our knowledge, our paper is the first to examine this issue empirically. Analysis results show that there is no strong interdependence between Bitcoin and preeminent financial indicators. These findings provide new information that will benefit policy makers, banks, financial investors, and risk managers in trading activities for both long-term and short-term strategies.
Application of the VAR model in examining the determinants of returns of selected cryptocurrencies
Bizinfo Blace
The increase in the value of cryptocurrencies, market capitalization, and volume of trading on crypto exchanges resulted in a significant increase in the interest of researchers in this decentralized financial system. The two most popular cryptocurrencies today - bitcoin and ethereum - have captured the greatest attention of researchers. Given that cryptocurrency trading is similar to stock trading, the author's assumption is that their returns are determined by the price of gold and the volatility index - VIX, representing this paper's research hypothesis. Testing through vector autoregression (VAR) models, Granger causality tests, and impulse response function (IRF) shows that gold returns do not impact, unlike the VIX volatility index and Ethereum, indicating a significant relationship between cryptocurrencies bitcoin and US stock markets. On the other hand, Bitcoin returns and the volatility index cause ethereum returns, while gold returns do not.
Istanbul Journal of Economics / İstanbul İktisat Dergisi
In this study, the relationship between the popularity of cryptocurrencies and their price, return and trading volumes are examined through time series analysis. The popularity variable is determined according the frequency of cryptocurrencies being searched on the internet. Stationarity of series is examined by Vogelsang and Perron (1998) structural breaks ADF unit root test. According to the test results, all series are found to be stationary at level values. VAR analyses and impulse-response functions are performed to reveal dynamic interaction between the series. According to impulse-response test results, returns of BITCOIN decreased against a decreasing shock in the number searches on the internet and its price and trading volume followed a fluctuating course. In order to see the causality relationship between variables the Granger causality test is conducted. Regression analyses are performed using ordinary least squares (OLS) method through three different equations. According to the result of the regression analysis, an increase in the number of internet searches for cryptocurrencies was found to positively affect prices, returns and trading volumes of all cryptocurrencies. The highest impact on prices and trading volume is observed in BITCOIN, while the highest effect on returns is observed in LITECOIN. According to the findings, popularity can be considered an important determinant for price, returns and trading volumes of cryptocurrencies.