As a popular cryptocurrency, Bitcoin has been an important investment tool in recent years. This study aims to analyze the factors that affect the Bitcoin price to help investors make better investment choices. Applying the simple linear regression model and Granger causality test to the data from January 2015 to December 2021, the research first examines the stationary of the data and then studies the relationships between Bitcoin price and other factors including Dow Jones Industrial Index, U.S. currency in circulation, U.S. disposable income. According to the result, all three factors have a positive effect on the price of Bitcoin and the Bitcoin price will in turn influence the Dow Jones Industrial Index and U.S. disposable income. This finding helps explain how certain economic indicators and Bitcoin prices interact. Since investment is always risky, investors must consider certain factors like the trend of DOW, M2, or PCI in advance to make a reasonable investment decision.
In the changing circumstances and the conflict between Russia and Ukraine, International crude oil prices rose sharply in the short term. This study will review the existing literature on the reason for the fluctuation of international crude oil prices and the dynamic change of Bitcoin, Tether, and Ethereum. This paper will also empirically evaluate the impact of fluctuation of international crude oil prices on the yield of electronic cryptocurrency. This research finds that the increase of futures crude oil prices will have a positive impact on the yield of electronic cryptocurrency, but this impact is short-term. Additionally, the growth of crude oil prices will not lead to the increase in the daily volatility of electronic cryptocurrency.
In the topic selection, we need to estimate the prices of bitcoin and gold according to the data given from 2001 to 2012. According to the estimated price, the initial amount is set as $1000, which is used as the principal for financial investment for a period of five years from 2016. In the whole modeling process, the main problems we need to solve are the following four points: the task 1 is the best investment strategy is given through the established model, and the investment value on October 9, 2021 is calculated. The task 2 is the best strategy of the model is proved. The task 3 is Determine the impact of transaction costs on transaction results. The last task is to Complete a memo with strategies, models, and results. In the whole modeling process, we first preprocess the data, which is arranged and classified in chronological order, and fill the data by interpolation fitting. LSMT algorithm is a neural network algorithm, which is suitable for the calculation of various long-term processes. The investment problem we study is a good application field. In the calculation process of the basic model, it is necessary to set the initial value, complete a series of processing, and process the hidden layer of LSTM unit. Take x as the output value, set the temporary hidden layer and new hidden layer, and verify that the size of the final output result is consistent with the label size. The hidden layer is transformed according to the sigmoid function proposed above. After calculating the hidden layer conversion, the error is back propagated, the input derivative of the file is obtained, and the overall error and record hidden layer are obtained. Then the calculated hidden layer difference is used to calculate the change of parameters and update parameters. Since bitcoin can be traded on any trading day, gold can only be traded on weekly trading days. For the convenience of calculation, we fix the transactions of bitcoin and gold as trading days every Friday. After receiving the benefits, the total assets of the cash flow as of the trading day are obtained by deducting the Commission to be paid. However, the model ignores the impact of bitcoin mining with different software and the fact that gold and bitcoin are not fixed on the same trading day, so there will be errors.
Md. Jamal Hossain, Mohd Tahir Ismail, Sadia Akter, Mohammad Raquibul Hossain
The popularity of Bitcoin increases with time and investors take it as an alternative investment due to continuous financial instability and uncertainty throughout the world.It can be an alternative not only for developed markets but also for emerging and frontier markets.Prior to now, researchers focused solely on developed markets.For this purpose, the present paper has explored the answer to the question of whether Bitcoin enables a hedge or diversifier or safe-haven against emerging and frontier stock market indices.Instead of previous analyses, here we have examined constancy relationships as well as time-varying relationships between Bitcoin with four stock indices of emerging and frontier stock markets of four different countries.We have applied the GJR-GARCH method to find the answer to the question, and we have also applied the Threshold Autoregressive (TAR) model for cross-validation of the findings.Our empirical results have shown that Bitcoin has safe-haven abilities are in normal and turmoil market situations for emerging and frontier stock markets.Also, we have found evidence of hedging and diversification properties.
This study aims to identify the main drivers of Bitcoin volatility. The empirical analysis is based on a dynamic Bayesian model averaging approach for twenty-two potential determinants. The results reveal that the most important factors for Bitcoin volatility are Google trends, total circulation of Bitcoins, US consumer confidence and the S&P500 index.
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 heavy-tailed and subject to extreme tail risks. We find strong, positive intra-market correlations, in particular with the two largest cryptocurrencies Bitcoin and Ethereum. No diversification effect can be achieved by aggregating market risks. On the contrary, a negligibly lower expected return and higher joint extreme returns can be observed. From this analysis, it can be concluded that investments in individual cryptocurrencies as well as in a portfolio show extreme risks of losses. From the investor’s point of view, a possible strategy of risk reduction through portfolio formation within cryptocurrencies is only promising to a limited extent and does not offer a satisfactory solution to significantly reduce the risk within this asset class.
Cryptocurrency has evolved from a fringe phenomenon to a far more popular method of investing and financing. For investors and traders, predicting the price of bitcoin is critical. Several machine learning algorithms are utilized to anticipate the price of digital money in this research paper. The analysis employed Decision Trees, Light Gradient Boosting Machines, and Neural Networks. The purpose of this study is to look at the predicted accuracy of each machine learning method. According to the analysis, decision tree, lightGBM, and neural networks have a very high accuracy rate when it comes to forecasting cryptocurrencies. These results shed light on guiding further exploration to help investors in building an appropriate digital currency portfolio and reducing risks.
This review focuses on blockchain technology, and its application and common problem with reference solution. The blockchain technology is nascent and complex and involves many different fields, which leads to the development of cryptocurrency. However, the crptocurrency has high volatility that demands prompt solution. Deep learning technology is considered as a promising approach to address this issue. After research, this paper develops four models with high efficiency and accuracy, including NLANN, JNN. LSTM and GRN to realize prediction in crptocurrency.
Abstract Developments in digital technologies are considered to be the most important innovations since the advent of the internet. In several countries, this has led to a significant change in the way payments are made, leading to new forms of payment, such as crypto-currencies. With regard to cryptocurrencies, it remains a complex issue involving especially volatility, but also money laundering and consumer protection issues. While most countries consider cryptocurrencies too volatile to be used as a payment alternative, crypto-currencies gain interest of investors in the last 10 years due to the possibility of obtaining large profits. The aim of the paper is to study the volatility of the first 5 cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Cardano and Ripple) through GARCH models. The process of evaluating highly volatile cryptocurrencies is complex and depends on many parameters. Therefore, our results would be particularly useful in terms of portfolio and risk management and could help them to be more agile in evaluating their investments, in making optimal decisions and making future forecasts. We find that the GARCH (1.1) models provide the best fit, in terms of modelling of the volatility in the most popular and largest cryptocurrencies. The results show that for BTC, ETH and XRP the appropriate model is GARCH (1.1) and in the case of BNC and CARDANO GARCH-M explain better the volatility of the crypto-currencies. Therefore, more in depth analysis of the datasets may be required to confirm or deny possible structural change. The study can be complemented by carrying out an event study on the 5 cryptocurrencies analyzed or extending the analysis by applying other GARCH models, to research the optimal model for several cryptocurrencies.
Çalışmanın temel amacı bitcoin ile BIST30 vadeli, altın vadeli ve döviz vadeli işlemler piyasası arasındaki volatilite etkileşimini araştırmaktır. Bu doğrultuda 25.07.2010 – 13.02.2022 dönemine ait haftalık veriler kullanılmıştır. Bitcoin ile BIST30 vadeli, altın vadeli ve döviz vadeli işlemler piyasası arasındaki volatilite etkileşimini araştırmak için çok değişkenli GARCH modellerinden DCC-GARCH modeli kullanılmıştır. Bitcoin, BIST30 vadeli, altın vadeli ve döviz vadeli işlemler piyasasında meydana gelen volatilitenin kalıcı olduğu tespit edilmiştir. Bitcoin ile BIST30 vadeli işlemler piyasası arasında çift yönlü, altın ve döviz vadeli işlemler piyasasında bitcoin’e doğru tek yönlü volatilite etkileşimi bulunmaktadır. Bitcoin ve BIST30 vadeli işlemler piyasası, altın vadeli işlemler piyasasından bitcoine doğru negatif yönlü etkileşim mevcuttur. Fakat döviz vadeli işlemler piyasasından bitcoine doğru volatilite etkileşimi ise pozitif yönde olduğu saptanmıştır.
The emergence of disruptive cryptocurrency platforms and decentralized finance (DEFI) has revolutionized the financial landscape over the last couple of years. Against this backdrop, and in view of the limited opportunities for diversification in the conventional assets, it becomes evident for the investors to look for better opportunities by searching for rather non-conventional assets like cryptocurrencies as a means of portfolio diversification. This study analyses the major cryptocurrencies based on their market capitalization and the major markets from MENA regions to evaluate the potential of cryptocurrencies in portfolio diversification. The study employs the mean-variance approach and then compares the results with the higher-order moments. We found that cryptocurrencies offer considerable potential for diversification for the MENA markets, but these exposures must be conservatively given the explosive price evolution and extreme volatility of the cryptocurrencies. The results also suggest that cryptocurrencies do not considerably contribute to the portfolio diversification in the uncertain market movements and crises, such as that witnessed during Fed's regime shift, accentuated by the Ukraine crisis.
Bitcoin derivatives positions are maintained with a self-selected margin, which is often too low to avoid automatic liquidation by the exchange, without notice, especially during periods of excessive volatility. Indeed, according to CryptoQuant, almost $80 billion of positions on centralised exchanges were liquidated during 2021, that is an average of over $200 million per day. So hedgers of bitcoin price risk should account for the possibility of automatic liquidation when taking positions on bitcoin futures. We derive a semi-closed form for an optimal hedging strategy with dual objectives – to minimize both the variance of the hedged portfolio and the probability of liquidation due to insufficient collateral. The solution depends on the statistical characteristics of the spot and futures extreme returns, and other parameters that characterize the hedger by choice of leverage, loss aversion and collateral management. An empirical analysis based on minute-level data compares the performance of the major direct and inverse bitcoin hedging instruments traded on five major exchanges.
Antonio Briola, David Vidal-Tomás, Yuanrong Wang, Tomaso Aste
We quantitatively describe the main events that led to the Terra project's failure in May 2022. We first review, in a systematic way, news from heterogeneous social media sources; we discuss the fragility of the Terra project and its vicious dependence on the Anchor protocol. We hence identify the crash's trigger events, analysing hourly and transaction data for Bitcoin, Luna, and TerraUSD. Finally, using state-of-the-art techniques from network science, we study the evolution of dependency structures for 61 highly capitalised cryptocurrencies during the down-market and we also highlight the absence of herding behaviour analysing cross-sectional absolute deviation of returns.
We examine whether the occurrence of jumps in the return of major cryptocurrencies increases the likelihood of jumps in the stock returns of blockchain and crypto-exposed US companies. We use two criteria to identify the US stocks with blockchain and cryptocurrency exposure; i) text search and ii) membership in the blockchain indices. We first detect that both asset classes are subject to jump behaviour. Then, we employ logistic regressions and show that the occurrence of jumps in some cryptocurrencies increases the probability of jumps in several blockchain and crypto-exposed companies. The co-jumping behaviour is not affected by the COVID-19 outbreak.