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.
Digital technology developments shape the behaviour, performances, standards of society, organizations and individuals imposing new ways of payments and new forms of money. In this environment in 2008 was developed a new type of currency, namely Bitcoin. Cryptocurrency, as this new form of money has been generically called, puts pressure on the traditional concept of money. Today, the economic value of cryptocurrencies is attested by their circulation and acceptance by user communities for trade. However, establishing this value raises debates in the literature. The research from this paper investigates and analyses if there is a strong enough connectedness between Bitcoin price evolution and energy consumption tendency (for mining), to influence Bitcoin value. Public data from January 2014 to July 2021 is used. An Artificial Neural Network (ANN) was used to study and predict the tendency of Bitcoin price and energy consumption. A comparison between the forecasting trend and the real trend (the evolution of energy consumption and Bitcoin price) was made. The conducted research starts with a quantitative one and ends with a qualitative one (trends). The obtained results show that qualitatively, there is a good correlation between monthly average values of BTC prices and electricity consumption for mining.
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
This research assesses the diversification patterns for tail dependence between Bitcoin return and trading volume by utilizing a dynamic mixture copula approach with spillover effect and asymmetric volatility effect. There are four main empirical findings. First, the spillover effect between return and trading volume exists. Second, the leverage effect is statistically significant for return and trading volume. Third, the linkages between return and trading volume are diversified. Both positive and negative tail dependence structures are observed, and the frequency of a positive tail dependence occurring is higher. Furthermore, the asymmetric tail dependence structure exists in positive and negative dependence situations. In the positive dependence structure, a co-movement in the increasing direction is stronger than a co-movement in the decreasing direction. In the negative dependence structure, a situation of a large return with low volume occurs more often than a situation of a small return with high volume. Fourth, the volatility of trading volume positively predicts the magnitude of positive dependence.
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.
The present research is reviewing the requirement of a novel approach in the case of cryptocurrency price prediction. Several types of research in the area of cryptocurrency and price prediction have been considered. Moreover, researches that are related to machine learning and deep learning are considered. The research paper considers the role of Artificial intelligence and machine learning in the price prediction of cryptocurrency. The issues in the case of previous research and the need for research in the area of cryptocurrency price prediction have been considered. The future scope of BTC, Wave, Ethereum, and Made cryptocurrency value prediction is also discussed.
Quantitative trading replaces the traditional subjective judgment mode based on modern digital model, so as to avoid irrational investment decisions under extreme conditions. This processing method has important research and application value in many fields such as stock and foreign exchange. Especially in the context of increasingly strong research on deep learning algorithms, judgment analysis on Cryptocurrency trading strategies and profitability has also received attention from all walks of life. Therefore, on the basis of understanding the current development of the stock market and the overall state of China's economy, this paper combines the stock prediction model with deep learning as the core, and constructs corresponding trading strategies on the basis of understanding the prediction results to improve the actual profitability.
Cryptocurrencies are becoming increasingly popular day by day among people. It is providing more features than traditional banking or money can provide, hence attracting investors all around the world. But people are still hesitant to use this technology because of the high price volatility are variable markets. The majority of the forecasting solutions reported have some level of error and cannot predict the price accurately due to randomness. The proposed model consists of using parent coins as a parameter for price prediction to overcome market volatility. Moving average is used as a data preprocessing technique for the effective prediction of Litecoin and Dogecoin prices. Also, in this, we are using Decision Tree, Random Forest, Extra- Tree-Regressor and Ridge regressor models for predicting the close price of cryptocurrencies. From the result, we can see that our proposed model performs better than predicting without using the parent coin feature.
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.
Bitcoin investment gained great research interest, especially after the onset of the COVID-19 pandemic, a period marked by huge volatility in this asset class. This study investigated Bitcoin’s persistence and hedging properties in the pre-COVID era to establish its efficiency and safety by testing relevant data. We evaluated the role of persistence in Bitcoin trading to highlight its efficiency. The GPH estimator and ARFIMA were used to map the evolving efficiency of the Bitcoin price. Our analysis of intra-day data exhibited the presence of an anti-persistence effect, following the popular conclusion of momentum and speculative trading in the Bitcoin market. The second section of this study evaluated whether Bitcoin played the role of a hedge and an asset of protection in a global portfolio manager’s portfolio during extreme market volatility. Using the Threshold GARCH (TGARCH), we evaluated the trading correlation between Bitcoin prices and four major indices, namely S & P 500, FTSE, Hang Seng, and Nikkei, on daily and weekly data. We identified the time-varying hedge and safety properties of Bitcoin: volatility, speculation, less-traded history, and lack of regulatory infrastructure. Our findings added to the literature by testing the efficiency of Bitcoin in major developed economies using returns of high-frequency data, along with daily returns. We also considered extreme movements in the currency to check its hedging and protection properties in a portfolio of developed market stocks. We recommended that investors be cautious when combining this currency with different stock markets based on our findings.
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 cryptocurrency market has gained popularity in the last few years. Additionally, there is the availability of data on price fluctuations on cryptocurrency exchanges. Thus, statistical analysis can be conducted to identify the characteristics of the cryptocurrency market. This chapter aims to identify the characteristics of the cryptocurrency market in India and clarify to what extent the cryptocurrency market is similar or different from the traditional financial market of stock, currency, derivatives, commodities, and bonds. Thus, this chapter presents the history and development of the cryptocurrency market in India, cryptocurrency exchanges operating in India, and the differences between the cryptocurrency market and the traditional financial market. The chapter also presents the analysis of fluctuation in prices of cryptocurrency on varied platforms such as CoinSwitch, Binance, CoinMarketCap, etc. The statistical properties of the cryptocurrency market are compared with the traditional financial market.
This chapter investigates the linkages and connections between different cryptocurrencies. Johansen cointegration and network analysis is employed to examine top eight cryptocurrenices (i.e., Bitcoin, Dogecoin, Stellar, Cardano, Tether, XRP, Ethereum Classic, and Chainlink). The study documents evidence to support cointegration among different cryptocurrencies. The study finds that cryptocurrencies Ethereum Classic, Chainlink, Dogecoin, and Bitcoin are connected as one group, and XRP, Stellar, and Cardano are connected as another group whereas Tether does not fall under any group and indicates no connection with other cryptocurrencies considered.
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.