This article aims to test a causal nexus between bitcoin market and economic policy uncertainty. We use the continuous wavelet analysis to investigate lead-lag relationship between bitcoin market and economic policy uncertainty in different time-frequency domains. Our findings show the negative relationship between bitcoin returns and economic policy uncertainty around the period of bitcoin’s currency recognition and COVIC-19 pandemic crisis both daily and monthly time series test. Furthermore, we find that the causality relationship between bitcoin and economic policy uncertainty is relatively indistinct around the period of bitcoin’s currency recognition, while bitcoin returns are leading economic policy uncertainty changes during COVID-19 pandemic crisis, indicating the economic policy uncertainty fluctuation trend can refer to the fluctuation of bitcoin, bitcoin can be viewed as a leading indicator, but it could not be employed as a safe-haven asset hedge against uncertainty during the period of COVID-19 pandemic.
This paper attempts to understand the dynamic interrelationships and financial asset capabilities of Bitcoin by analysing several aspects of its volatility vis-a-vis other asset classes. This study aims to analyse the volatility dynamics of the returns of Bitcoin. An asymmetric GARCH model (EGARCH) is used to investigate whether Bitcoin may be useful in risk management and ideal for risk-averse investors in anticipation of negative shocks to the market (leverage effect). This paper also examines Bitcoin as an investment and hedge alternative to gold as well as NSE NIFTY using a multivariate DCC GARCH model. DCC GARCH models are also used to check whether correlation (co-movement) between the markets is time-varying, examine returns and volatility spillovers between markets and the effect of the outbreak of COVID-19 in India on the investigated markets. The results show that given the supply of Bitcoin is fixed, low returns realisation is equivalent to excess supply over demand wherein investors are selling off Bitcoin during bad times. The positive co-movement between Bitcoin and gold during the COVID-19 outbreak shows that investors perceived Bitcoin as a relatively safe investment. However, overall analysis shows that Bitcoin was not considered a safe hedge and an investment option by Indian investors during the study period.
Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume based on high-frequency data representing two major cryptocurrencies: bitcoin and ether. We apply the multifractal detrended cross-correlation analysis, which is considered the most reliable method for identifying nonlinear correlations in time series. We find that all the quantities considered in our study show an unambiguous multifractal structure from both the univariate (auto-correlation) and bivariate (cross-correlation) perspectives. We looked at the bitcoin--ether cross-correlations in simultaneously recorded signals, as well as in time-lagged signals, in which a time series for one of the cryptocurrencies is shifted with respect to the other. Such a shift suppresses the cross-correlations partially for short time scales, but does not remove them completely. We did not observe any qualitative asymmetry in the results for the two choices of a leading asset. The cross-correlations for the simultaneous and lagged time series became the same in magnitude for the sufficiently long scales.
This is the first study to examine the quantile connectedness for returns-volume and volatility-volume pairs for the three non-fungible tokens (THETA, Tezos, and Enjin Coin) using the quantile VAR approach. The results report the highest connectedness of volume with returns and volatility in the extreme upper quantile compared to other quantiles, implying the asymmetric connectedness. The spillover effect is observed from volume to returns and volatilities in extreme upper and lower market conditions, whereas opposite direction of spillovers is evident for the selected non-fungible tokens at median quantile. Our findings are useful for investors in predicting the returns and risk of NFTs using trading volume in the extreme market conditions.
In this paper, we use Analytic Hierarchy Process (AHP) to determine the appropriate weights and establish a bull market and bear market judgment indicator and investment risk models. Secondly, the Autoregressive Integrated Moving Average model (ARIMA) is constructed to make the expected trend for the next five years, and the optimal asset portfolio of gold assets and bitcoin assets is constructed through quadratic programming, and the Dynamic Programming (DP) model is used to compare strategies between two trades. Finally, the rationality of the model calculation is verified by the test of Long-Short-Term Memory (LSTM).
This study analyzes whether Bitcoin, gold, oil, and stock have the ability to hedge against inflation in high cryptocurrency adoption countries in the periods from January 2010 to March 2021. It is hypothesized that the assets behave differently and thereby respond differently to inflation in different market conditions. Therefore, we employ the Markov Switching Vector Autoregressive to examine these assets’ hedging ability against inflation in both stable and turbulent market regimes. Our main findings are threefold: We show that there exists a structural change and nonlinear relationship between the returns of hedging assets and inflation. Second, all assets can hedge against inflation more effectively in the short run than in the long run. We find that the inflation hedging ability of these assets are weak in the long run for both market regimes. We also find some evidence that the rigidity between the assets and inflation is relatively high in the stable regime. Third, according to the impulse response analysis, we also find that the responses of assets to inflation shock are heterogeneous across two market regimes.
The emergence of digital currency is becoming prevalent in the age of globalization – specifically, cryptocurrencies, a subset of digital currency that encompass revolutionary technology. This study postulates that certain governments are more prone to adopting cryptocurrencies, especially those seeking to eschew international sanctions and protect corrupt practices. Three comparative case studies focus on countries (Iran, Russia, and Venezuela) that share attributes that result in adopting what has been called “native cryptocurrencies”: corruption, GDP level, economic volatility, and Western sanctions. KEYWORDS: Cryptocurrency; Blockchain; Political Science; Law; Foreign Sanctions; Government; Iran; Russia; Venezuela
Over the past years, cryptocurrencies have drawn substantial attention from the media while attracting many investors. Since then, cryptocurrency prices have experienced high fluctuations. In this paper, we forecast the high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling volatility to select the best model. We propose various generalized autoregressive conditional heteroscedasticity (GARCH) family models, including an sGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1), EGARCH(1,1), which we compare to a multivariate DCC-GARCH(1,1) model to forecast the intraday price volatility. We evaluate the results under the MSE and MAE loss functions. Statistical analyses demonstrate that the univariate GJR-GARCH model (1,1) shows a superior predictive accuracy at all horizons, followed closely by the TGARCH(1,1), which are the best models for modeling the volatility process on out-of-sample data and have more accurately indicated the asymmetric incidence of shocks in the cryptocurrency market. The study determines evidence of bidirectional shock transmission effects between the cryptocurrency pairs. Hence, the multivariate DCC-GARCH model can identify the cryptocurrency market’s cross-market volatility shocks and volatility transmissions. In addition, we introduce a comparison of the models using the improvement rate (IR) metric for comparing models. As a result, we compare the different forecasting models to the chosen benchmarking model to confirm the improvement trends for the model’s predictions.
Aiming at the portfolio problem of gold and bitcoin with a given linear trading commission, this paper puts forward the stage implementation forecast and optimal portfolio model. In the aspect of data prediction, SMA is used to predict the initial data, LSTM is used to predict the price trend of long-term data, and daily updated real-time price data is predicted. Considering the risk aversion of investors, the heuristic algorithm is used to solve the daily trading strategy of maximizing utility from September 12th, 2016 to September 12th, 2021. The simulation analysis of the sliding window shows that the algorithm can realize reasonable prediction, which verifies the effectiveness of the algorithm.
Umair Khalid, Syeda Asnia Arif, Muhammad Umair Khan
Purpose: The research aims to analyze the log-returns of Bitcoin exchange rates against the US Dollar and Chinese Yuan by applying parametric distributions for understanding behavior and suggesting a best-fitted distribution.
 Design/Methodology/Approach: Methodology involves the volatility risk analysis using the GARCH model for analyzing the behavior of Bitcoin Exchange rates of USD and CNY.
 Findings: The results showed that the Weibull distribution gives the best fit to both of the currencies’ exchange rates
 Implications/Originality/Value: The exchange rates of Bitcoin analyzed in this study in midst of myriad other cryptocurrencies using parametric distributions thereby encouraging the application of nonparametric and semiparametric distributions in similar scenarios. The application of this study would enable not only individual investors but also institutional investors and venture capital firms to stay informed of alternating trends and movements through distributions for predicting future returns.
This study examines the responses of Bitcoin and gold to categorical financial stress and compares the responses before and during the COVID-19 pandemic. The OLS and Quantile regression estimations revealed that gold and Bitcoin exhibit similar reactions in full and pre COVID-19 samples. Gold and Bitcoin respond positively to equity valuation and safe assets categories of financial stress. Gold also reacts positively to the credit category of financial stress suggesting that widening credit spreads are bullish for gold. Bitcoin and gold respond differently in the funding category, and there is no significant reaction to volatility-related financial stress. Overall, the effects of categorical financial stress on gold and Bitcoin are similar in the full sample and sub-sample before COVID-19, but the effects are heterogeneous. Interestingly, during the pandemic, the reactions of gold and Bitcoin to categorical financial stress have changed. Gold only reacts positively to the credit category of financial stress across quantiles. Bitcoin reacts positively to credit and safe asset categories but not across all quantiles. The findings offer insights into the effects of several systemic financial stress on the value of safe haven assets.
Blockchain-based cryptocurrencies have gained popularity in television and digital media channels with the highest value records of all time broke in a row, both in academic studies and in recent times. In the framework of the study conducted to provide data to those who want to assess their investments in blockchain-based cryptocurrencies. In the research it is aimed to examine correlation between Bitcoin as an independent variable and S&P500 Index, US 10-year Treasury and altcoins like Ethereum, Cardano, Chainlink with Granger causality test. Findings shows that Chainlink as an investment tool has the highest return with 6.22% and it is followed by Cardano with 5.74%, Ethereum with 5.20% and ultimately Bitcoin. The US 10-year Treasury offers not only the lowest rate of return with 10% loss but also riskier tool than Bitcoin. S&P500 Index offers lower rate of return and riskier in comparison with FED interest rate. According to the covariance values, it has been determined that Bitcoin has an increasing linear relationship with Ethereum, Cardano and Chainlink, and a decreasing linear relationship with the FED interest rates and US 10-year Treasury, while it is unrelated to the S&P500 Index.
The objective of this article is to analyze the co-movements in the G7 stock markets, such as DJ index, S&P500 (representing the USA stock market), FTSE 100 (United Kingdom), S&P/TSX (Canada), DAX 30 (Germany), CAC 40 (France), Nikkei 225 (Japan), Italy Ds market (Italy) and the cryptocurrencies Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH) and Crypto 10, during the period of February of 2018 to November of 2021. The results show that the cryptocurrencies BTC, ETH, and LTC increase the co-movements between their pairs, while the Crypto 10 index reduces the number of shocks when compared with the sub-period before COVID-19. Regarding the stock markets, DJ index kept the same level of shocks, whereas the Nikkei 225 decreased. For Germany (DAX), EUA (S&P500), Canada (S&P/TSX), United Kingdom (FTSE 100), France (CAC40), and Italy (Italy Ds Market) markets the results show an increase in movements during the global pandemic period. It is then possible to conclude the existence of evidence regarding synchronization and high co-movements, the results put at risk the implementation of efficient portfolio diversification strategies. These conclusions also open space for the market regulators to take steps to ensure better information on the dynamics of the international financial markets.
Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. Methodology: The parametric GARCH models to characterize the volatility of Bitcoin returns are widely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. Results: We show that the volatility forecast of the nonparametric GARCH model yields superior performance compared to an extended class of parametric GARCH models. Originality / relevance: The improved accuracy of forecasting the volatility of Bitcoin returns based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to commonly used GARCH parametric models.
It has been emphasized in many studies that the developments in the crypto money markets have a serious impact on the world stock markets. Due to these effects, the fluctuations in the world stock markets have increased, and it has become necessary for investors to follow these markets more closely and determine their strategies according to these developments. In this study, it was examined whether the developments in the crypto money market have an effect on Borsa Istanbul (BIST) indices. For this purpose, data of the three most popular cryptocurrencies Bitcoin, Ethereum and Ripple were used, and their spillover effects on BIST100, BIST30 and banking (XBANK) indices were investigated. Oil prices (WTI) and fear index (VIX) variables were also used as control variables in the study. The findings obtained from the analyses in our study carried out for the period 01/01/2014-31/12/2021 showed that there is a positive spillover effect from the crypto money markets to the indices we examined. While oil prices were found to be statistically significant in all models among the control variables, different results were obtained on the effect of the fear index. The findings show that it is imperative for stock market investors to closely monitor the developments in the crypto money market in addition to track various economic variables, in their investment decisions.
This study proposed an optimal model to examine the relationship between the Bitcoin price and six macroeconomic variables – the Bitcoin price, Standard and Poor's 500 volatility index, US treasury 10-year yield, US consumer price index, gold price and dollar index. It also examined the effectiveness of the vector error correction model (VECM) in analyzing the interrelationship among these variables. The authors employed the following approach: first, the authors sampled the period August 2010–February 2022. This is because Bitcoin achieved a market capitalization of more than US$1 tn over this period, gaining market attention and acceptance from retail, corporate and institutional investors. Second, the authors employed a VECM with the six macroeconomic variables. Finally, the authors expanded the long-run equilibrium relationship (time-invariant cointegration)-based VECM to develop a time-varying cointegration (TVC) VECM. The authors estimated the TVC VECM using the Chebyshev polynomial specification based on various information criteria. The results showed that the Bitcoin price can be modeled with the VECM ( p = 1, r = 1). The TVC approach generated more explanatory power for Bitcoin pricing, indicating the effectiveness of the approach for modeling the long-run relationship between Bitcoin price and macroeconomic variables.
Due to its growing popularity and commercial acceptance, cryptocurrency is playing an increasingly essential role in altering the financial system. While many people are investing in cryptocurrency, the dynamic characteristics and predictability of cryptocurrency are still largely unknown, putting investments at risk. In this paper, we attempt to anticipate the Bitcoin price by taking into account a variety of factors that influence its value with the highest possible accuracy using (LSTM) Recurrent Neural Network. The data we use in this work includes updated daily records of many aspects of Bitcoin pricing over a five-year period. Since the cryptocurrency (Bitcoin) data is so volatile, we implement an effective pre-processing of the data in order to have a better prediction result. With this solution, we gain accuracy of 95.7% and RMSE of 0.05. Furthermore, we compare this work with other existing methods based on performance and accuracy. This comparison demonstrates that utilizing LSTM with adequate hyperparameter tweaking is one of the most efficient ways for cryptocurrency price prediction.
Blockchain and cryptocurrency are gradually going mainstream with new cryptocurrencies introduced every single day. The speculative nature of these digital assets expose their prices to large fluctuations. Trading these crypto-assets necessitate an adequate understanding of this emerging market as well as adequate tools to model the market risk and efficient allocation of funds. This may assist crypto investors in taking advantage of the highly volatile aspects of these assets. The portfolio consider in this study consists of six cryptocurrencies: four traditional cryptocurrencies (BTC, ETH, BNB and XRP) and two stablecoins (USDT and USDC). We examine the copula particle swarm optimization (CPSO) portfolio strategy against three other portfolio strategies, namely, the global minimum variance (GMV), the most diversified portfolio (MDP) and the minimum tail dependent (MTD). CPSO appears to be a promising strategy during extreme market conditions while GMV seem favorable during normal market conditions. Most importantly, hedge and safe-havens ability of the two stablecoins is clearly exhibited with CPSO, while their diversification property is inhibited.
It is possible to define uncertainty as the variability of conditions, the ambiguity and obscurity of statements and events. Uncertainty, for whatever reason, affects the economy in different ways. Uncertainty causes people to be more concerned about their future income. Various estimation and methods have been developed in recent years to calculate the uncertainty, which is equivalent to the concept of uncertainty. These indices, in which economic and political uncertainties are calculated, appear as a form of calculation that also includes political discourses along with financial risk. The aim of this study is to examine the causality relationship between the Global economic political uncertainty index and Bitcoin electricity consumption. For this purpose, the Toda-Yamamoto causality test was applied using data from the period 2011:M7-2022:M1. According to the obtained Toda-Yamamoto causality test findings, Granger causality relationship has been determined both from the global economic-political uncertainty index to Bitcoin electricity consumption and from Bitcoin electricity consumption to the global economic-political uncertainty index.