The Covid-19 pandemic affected financial markets in several ways, influencing the dynamics of the relationships between asset classes. We investigate the connectedness between cryptocurrencies and international energy markets from 2018 to 2021 using the time-varying parameter vector autoregression approach. Net total directional connectedness suggests that the cryptocurrency and energy indexes had heterogeneous roles. Bitcoin and Ripple coin were the net receivers of shocks, while Ethereum switched from receiver to transmitter. The US energy market was a persistent net transmitter of shocks, while Asian energy markets were consistent net shock receivers. Pairwise connectedness reveals that cryptocurrencies can explain the volatility of the energy markets during the difficult period of the pandemic at the beginning of 2020. We provide insights for portfolio optimization and policy implications.
This study will investigate the liquidity spillover effects of five cryptocurrencies: Bitcoin, Ether, Binance-coin, Ripple, and Tether. Firstly, the researcher utilizes the Amihud illiquidity ratio to quantify the liquidity performance of the five currencies, which we treat as weekly for the purposes of our study due to data collecting constraints. Secondly, to quantify the liquidity spillover effect in the cryptocurrency market over the period of 2017-2022, the researcher employs Diebold and Yilmaz's spillover index. The results identify the senders and receivers of liquidity spillovers on an individual and pairwise basis for the five major currencies and demonstrate the presence of time variation. Additionally, this paper evaluates the news report-based cryptocurrency uncertainty index (UCRY). This includes the price of cryptocurrencies (UCRY price) and the uncertainty surrounding cryptocurrency policy (UCRY policy). Considering the constructed index follows the same path as the largest cryptocurrency, Bitcoin, it is therefore recommended that the Bitcoin price can be used to forecast the cryptocurrency uncertainty index. Overall, this study has filled a gap in the literature by conducting research on liquidity spillovers in cryptocurrency markets, and it presents some preliminary conclusions. However, in order to verify the validity of our findings and to provide more meaningful results, additional research is required over a longer time horizon and with additional cryptocurrency types.
Abstract This article examines the relationship between Bitcoin volume and term deposit investments in Mexico, Indonesia, Nigeria, and Turkey (MINT) from 2016 to 2021. We run cointegration and error-correction econometric models for each country, analyzing both the long-term and short-term interactions between Bitcoin volume and time deposits. Our findings indicate a negative association between Bitcoin volume and term deposits in all the MINT countries, except Mexico. This suggests that individual investors in economically and financially unstable nations are increasingly turning to Bitcoin as an alternative investment option. The observed effects, while currently modest, highlight the potential threats posed by decentralized cryptocurrencies to the monetary systems of emerging economies, impacting the stability of the banking industry and overall economic growth.
This paper examines the impact of Bitcoin futures introduction on the crash risk of spot Bitcoin prices. Using both time-series regression with a time dummy and a difference-in-differences (DID) framework, we find that crash risk, proxied by the negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL) of 5-minute intraday Bitcoin returns, declines significantly after the launch of Bitcoin futures. Robustness checks confirm that the findings are robust to changes in control variables, control cryptocurrencies, the sampling frequency for high-frequency returns, and an extended post-introduction period. Furthermore, we explore the moderating roles of market liquidity and investor attention. The crash-mitigating effect of Bitcoin futures is significantly more pronounced in periods of low liquidity and limited investor attention, suggesting that futures markets play a stronger role in enhancing information efficiency under such conditions.HighlightsThis paper examines whether Bitcoin futures introduction increases or decreases Bitcoin price crash risk.The price crash risk of Bitcoin, measured by NCSKEW and DUVOL from high-frequency intraday data, decreases significantly after futures introduction.The main findings are robust to changes in control variables, control cryptocurrencies, the sampling frequency for high-frequency returns, and an extended post-introduction period.The crash-mitigating effect is more pronounced in periods of low liquidity and limited investor attention.
Bitcoin and Ethereum are the top two cryptocurrencies in the first and the second places respectively. This study looks to examine the inter and intra dynamics and relationship between Bitcoin price (BTCP), Ethereum price (ETHP), Bitcoin volume (BTCV) and Ethereum volume (ETHV). This study utilizes the Johansen Cointegration Test as well as the Vector Error Correction Model (VECM) to determine the long-run relationship between Bitcoin price (BTCP), Ethereum price (ETHP), Bitcoin volume (BTCV) and Ethereum volume (ETHV) before and during the COVID-19 pandemic and to determine whether the pandemic has any effect on the changes in prices and volumes of these cryptocurrencies. The study also utilizes daily data extracted from coinmarketcap.com for Bitcoin price and volume as well as for Ethereum price and volume from August 8, 2015 up to February 28, 2021 extracted on March 1, 2021. We find that the COVID-19 pandemic has no effect on the long run relationship between Bitcoin price (BTCP), Ethereum price (ETHP), Bitcoin volume (BTCV) and Ethereum volume (ETHV) for all the specifications. We also find that the pandemic has no effect on the prices of Bitcoin and Ethereum but has an effect on their trading volumes in the short run. We find that the price of Bitcoin is positively related with the Bitcoin trading volume and positively related with the trading volume of Ethereum whereas the Ethereum price is negatively related with the Bitcoin trading volume and positively related with the trading volume of Ethereum. We also find that the price of Bitcoin is positively related with the trading volume of Bitcoin and negatively related with the trading volume of Ethereum. On the other hand, the price of Ethereum is positively related with trading volume of Bitcoin and positively related with the trading volume of Ethereum.
Shinta Amalina Hazrati Havidz, Ni Putu Indah Rahmadani, Priscilla Laura Aditya Tori
This research was conducted to determine whether gold and cryptocurrency (i.e., Bitcoin) can be used as safe haven assets for oil, wheat, stock index (SI), government bond (GB), Islamic stock (IS), and Islamic bond (IB) during the Russia-Ukraine war. We used panel quantile regression by utilizing extreme lower quantiles (i.e., 1%, 2.5%, 5%). It will only be recognized as a safe haven asset if it is negatively correlated with another asset during extreme adverse shocks. The data spans from 23 February 2021 – 25 July 2022 which covered the five largest economies in Europe and Asia (i.e., Germany, France, the UK, China, and Japan). The findings indicate that gold only acted as a safe haven asset for wheat, SI, and IS during the Russia-Ukraine war. Additionally, Bitcoin only serves as a safe haven asset for oil, wheat, SI, and GB during the Russia-Ukraine war.
In this paper, we compare the predictive power of Auto Regressive Integrated Moving Averages (ARIMA) and Multi-Layer Perceptron Artificial Neural Networks (MLP ANN) model to short-term forecast the monthly returns of Bitcoin cryptocurrency. We evaluate the performance of two models using time series with monthly data from January 2018 to December 2021. The key parameters for the final assessment of prognostic models are the values of Root Mean Square Error-RMSE and Forecast Error-FE. The results of the short-term BTC return forecast showed better properties of composite compared to univariate time series forecasting models, i.e., higher prognostic power of the MLP ANN model compared to the selected ARIMA (1,1,3) model (lower RMSE and FE). The results point to further comparative research of prognostic models and the possibility of forming more complex and hybrid structures of neural network models in order to predict economic phenomena as accurately as possible.
The main goal of this research is to evaluate the returns and risks of the following types of assets: Bitcoin, EUR Stoxx 50, gold, bonds: government bonds ICE Bof A 1-10 Year excluding Italy and Greece and the corporate bond index ICEB of A 1-10 Year AA. The paper tested a total of ten portfolios according to different scenarios for digital and financial assets. Also, in the paper, greater measures of risk and return were calculated with the aim of forming an optimal portfolio with minimal risk. The results of this research revealed that the correlation between Bitcoin and other forms of financial assets is generally low and negative, which can be a good instrument for portfolio diversification, and positively affect portfolio performance. Also, the results of this study showed that in terms of volatility and return measure of a total of ten portfolios, the second portfolio (whose structure consists of Bitcoin, Euro Stoxx 50, gold, government bonds ICE Bof A 1-10 Year - excluding Italy and Greece and the corporate index bond ICEBof A 1-10 Year AA) is the most optimal portfolio. The findings of this research can serve in risk and loss assessments of portfolio managers, investors, and regulators.
This research paper explores the relationship between the global economic policy uncertainty index (GEPU) and Ethereum price.By employing the Hodrick-Prescott Filter Decomposition, the price of Ethereum is decomposed into a trend component, which reflects the increasingly wide usage, and the cyclical component, which shows its character as a safe haven asset and a speculative financial asset.By examining the relationship between the GEPU and the cyclical component of Ethereum, I find that GEPU Granger causes cyclical Ethereum, and they have a cointegration relationship.Their error correction models also demonstrate that cyclical Ethereum responds in the short-run to changes in GEPU and deviations from long-run equilibrium.The dynamics make the cyclical Ethereum converge towards their long-run equilibrium relationship.
Cryptocurrencies are considered to be among the most disruptive innovations done in the financial sector within the last decade. It is a digital asset that is designed to serve as a medium of exchange using cryptography. Financial modeling of cryptocurrencies is needed in order to determine the presence of dependence between currencies. Copulas functions assist in modeling dependency structure by making it possible to separate marginal distributions of a given multivariate distribution. The purpose of the study was to model dependencies of cryptocurrencies using copula Garch. The study proposed the use of copula Garch model to model the dependence of cryptocurrency price data. Bivariate copula was extended to Bivariate Copula Garch in order to model prices and measure the cryptocurrency dependence. Prices of the four cryptocurrencies (Bitcoin, Binance, Litecoin and Dogecoin) were analyzed to establish whether there exists any dependency. The results showed standard Garch (1,1) under the highly flexible ARMA-GARCH model was appropriate to identify the true patterns of index returns. Fitting the copula standard Garch (1,1) model to the currencies, it was observed that the pair Litecoin and Bitcoin has the highest tail dependence among the selected cryptocurrencies, which implies that change in prices of Litecoin will influence the prices of Bitcoin and vice versa is true. Optimization of the cryptocurrencies showed that Dogecoin has the best optimization. The results of this study indicate that investing on Dogecoin significantly reduces risk irrespective of significant correlation among Litecoin, Bitcoin and Binance. Standard Garch (1,1) is the best in identifying dependence between the cryptocurrencies.
In this paper, we studied the extreme connectedness between Bitcoin and crypto-mining stocks using the quantile connectedness approach of Ando et al. (2022). We estimated the connectedness (i.e., the direction and strength of spillover effects) at the median, extreme lower, and extreme upper quantiles. Our results revealed a highly interconnected system, with Bitcoin identified as a net transmitter of shocks. RIOT and MARA also emerged as major net transmitters in the system, while GREE and NILE were net receivers. The spillover effects were more pronounced during extreme market conditions compared to normal conditions. Moreover, the connectedness of the system progressively increased, peaking in 2021 when China banned crypto-mining. The extreme and dynamic connectedness identified in this study offers valuable insights for investors regarding hedging strategies and portfolio allocation, as well as for regulators focused on financial stability and systemic risk.
Exchange-traded funds (ETFs) investing in bitcoin futures contracts first listed for trading in the fall of 2021. This research evaluates the extent to which the returns of bitcoin futures and bitcoin correspond to determine if bitcoin futures provide an effective proxy for a direct bitcoin investment. A no-arbitrage framework for bitcoin futures is established, which provides the basis for the empirical analyses that follow. The empirical analyses of returns correspondence between bitcoin futures and bitcoin use daily and monthly returns to estimate single-factor asset pricing regressions, finding coefficients of expected magnitude and that bitcoin returns explain over 97% of the variation in bitcoin futures returns. This research also estimates two-factor asset pricing regressions that include a novel excess carry term. The two-factor regressions find statistically significant excess carry term coefficients and over 99% explained variation. Finding strong evidence that the returns of bitcoin futures and bitcoin closely correspond, this research concludes that bitcoin futures provide an effective proxy for a direct bitcoin investment.
Bao Doan, Dulani Jayasuriya, John B. Lee, Jonathan J. Reeves
In this study, we analyse systematic risk associated with the two leading cryptocurrencies - Bitcoin and Ethereum, from 2015 to 2023. Our findings show a significant escalation in the systematic risk levels, with beta estimates rising from 0.032 to 0.834 for Bitcoin, and from 0.087 to 1.003 for Ethereum. This hike in risk levels has dramatically reduced the diversification benefits of cryptocurrency that were documented in prior studies. In addition, we also identify increased autocorrelation of cryptocurrency systematic risk.