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.
Bitcoin has indeed been widely considered as an asset class in recent years, after the asset bubbles of cryptocurrency prices. Because of its extreme volatility, it requires accurate forecasts on which to base financial decisions. Although current research has used machine learning to improve Bitcoin price prediction accuracy, few have looked into the viability of using alternative modelling algorithms on samples with varying data formats and dimensional attributes. To use machine learning approaches to predict Bitcoin price at various frequencies, we first divide Bitcoin price categorized as everyday price and high-frequency price. For Bitcoin exchange rate forecasting, a collection of high-dimension features such as property and network, trade and market, attention, and gold spot price are employed, while good trading features obtained from a bitcoin wallet are being used for five-minute periodic price prediction. The goal of this study is to see how well machine learning models can predict the price of Bitcoin in relation to the US dollar. The learning models are linear regression, random forest, support vector machine, ARIMA, LSTM, and RNN are analyzed in this work. The importance of the sample dimension in machine learning methods is shown in our analysis of bitcoin price prediction. Our research results show that the recurrent neural network outperformed all other models with lower MAPE and RMSPE as 0.3174, 0.8853 respectively.
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.
Virtual kriptovalyuta olan bitkin rəqəmsal formata malik olan, texniki olaraq blokçeyn kimi ifadə edilən əməliyyatları əhatə edən və mərkəzi pul sisteminə daxil olmayan valyutadır. Tədqiqatda kriptovalyuta Bitcoin ilə valyuta məzənnələri arasındakı əlaqəni ortaya çıxarmaq hədəflənir. ABŞ Dolları ilə Avro, Yapon Yeni, İngilis Funtu, Avstraliya Dolları, Kanada Dolları, İsveçrə Frankı, Yuan Renminbi və İsveç Kronu məzənnələri ilə Bitcoin məzənnəsi arasındakı əlaqə, 3.02.2016- 04.10.2020 tarixləri arasındakı gündəlik məzənnələrə əsaslanaraq, struktur Qreqori və Hansen kointeqrasiyasını və Qrencer səbəb-nəticə analizini pozur. Təhlil nəticəsində müəyyən edilib ki, BTC/USD məzənnəsində struktur fasilələri 2017-cİ ilin aprel və dekabr aylarında baş verib. Bundan əlavə, tədqiqatda valyuta məzənnələrinin zaman silsiləsi arasında uzunmüddətli kointeqrasiya əlaqəsi, CNY/USD məzənnəsi ilə BTC/USD məzənnəsi arasında isə birtərəfli müsbət səbəb əlaqəsi müəyyən edilmişdir. Açar sözlər: kriptovalyuta, bitcoin, valyuta məzənnəsi, struktur fasilə, zaman seriyasi analizi Gunay Samir Karimli Relationship between cryptocurrency and rates Abstract Bitcoin, a virtual and cryptocurrency, is a digital currency that encompasses transactions, technically referred to as blockchain, and is not part of the central monetary system. The study aims to uncover the link between the cryptocurrency Bitcoin and exchange rates. The relationship between the US dollar and the euro, the Japanese yen, the British pound, the Australian dollar, the Canadian dollar, the Swiss franc, the yuan renminbi and the Swedish krona and the Bitcoin exchange rate, based on the daily exchange rates between 3.02.2016 and 04.10.2020, and Grenzer violates cause-and-effect analysis. The analysis revealed that structural breaks in the BTC / USD exchange rate occurred in April and December 2017. In addition, the study identified a long-term cointegration relationship between exchange rates over time, and a one-way positive causal relationship between the CNY / USD exchange rate and the BTC / USD exchange rate. Key words: cryptocurrency, bitcoin, exchange rate, structural break, time series analysis
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.
In the actual trading process, investors can only give the best daily trading strategy based on the past price data of gold and bitcoin, then they need to predict and evaluate the trend of the investment items in the coming period and plan out the trading scheme in advance. We also draw on data from many investment questionnaires on websites such as Stock Market Analysis & Tools for Investors to give specific trading strategies. We choose the XGBoost regression price prediction model and enable the genetic algorithm to find the best learning rate parameters. The first 100 trading days of gold and bitcoin data are taken separately for learning training tests, and then the first 20 data are used to predict the price trend for the next five days, which is repeated every day. It provides more accurate prediction results based on the latest prices. An optimization model is established to increase the final investment value by judging the buying and selling indexes by whether the expected return exceeds the purchased commission.
Propósito. Esta investigación tiene como objetivo identificar la literatura científica existente en torno al valor y precio de los NFTs. Metodología. La metodología utilizada en el presente artículo consta de un análisis bibliométrico, se usa la base de datos Scopus desde la primera aparicion de un artículo hasta la actualidad. Hallazgos. Los resultados muestran que existe muy poca literatura científica entorno al valor y precio de los NFTs, se logró identificar ocho artículos, en donde solamente tres contribuyen en su totalidad a la descripción de las variables valor y precio, además se encuentran variables incidentes en el valor y precio de los NFTs lo que llevaría a tener el potencial de generar nuevo conocimiento en este ámbito, realizar propuestas teóricas en modelos de valuación y precio para estos activos. Originalidad.La investigación se realizó considerando solamente a la base de datos Scopus utilizando el software VosViewr, se recomienda para futuras investigaciones tomar en cuenta otras bases de datos.
Purpose The purpose of the present study is to contribute to the existing literature by examining the nexus and the connectedness between classes S&P Green Bond Index, S&P GSCI Crude Oil Index, S&P GSCI Gold, MSCI Emerging Markets Index, MSCI World Index and Bitcoin, during the pre-and post-Covid period beginning from August 2011 to July 2021 (10 years). Design/methodology/approach The study employs time-varying parameter vector autoregression and Quantile regression methods to understand the impact of events on traditional and upcoming asset classes. To further understand the connectedness of assets under consideration, the study used Geo-Political Risk Index (GPR) and Global Economic Policy and Uncertainty index (GPEU). Findings Findings show that these markets are strongly linked, which will only expand in the post-pandemic future. Before the pandemic, the MSCI World and Emerging Markets indices contributed the most shocks to the remaining market variables. Green bond index shows a greater correlation and shock transmission with gold. Bitcoin can no longer be used as a good hedging instrument, validating the fact that the 21st-century technology assets. The results further opine that under extreme economic consequences with high GPR and GPEU, even gold cannot be considered a safe investment asset. Originality/value Financial markets and the players who administer and communicate their investment logics are heavily reliant on conventional asset classes such as oil, gas, coal, nuclear and allied groupings, but these emerging asset classes are attempting to diversify.
The possibility of including financial instruments, such as equity, debt, derivative and market-based funds, in a portfolio varies with their market sensitivity. Cryptocurrency (crypto) has been of recent origin and interest to investors and policymakers. The study has attempted to explore opportunities for Indian and international investors in equity and crypto markets. Bivariate analysis between the crypto index and Indian market indices revealed few causal linkages between crypto and other indices. Standard VAR and Granger causality have been used for exploring the association between the variables. DCC-GARCH has been applied for checking further on volatility spillover and the relationship between indices. Granger results indicate the presence of linkages between crypto and energy, media, and oil & gas indices. However, spillover results have shown an absence of such linkages in the short run but a significant presence in the long run except for a few indices.
Tether Limited has the sole authority to create (mint) and destroy (burn) Tether stablecoins (USDT). This paper investigates Bitcoin's response to USDT supply change events between 2014 and 2021 and identifies an interesting asymmetry between Bitcoin's responses to USDT minting and burning events. Bitcoin responds positively to USDT minting events over 5- to 30-minute event windows, but this response begins declining after 60 minutes. State-dependence is also demonstrated, with Bitcoin prices exhibiting a greater increase when the corresponding USDT minting event coincides with positive investor sentiment and is announced to the public by data service provider, Whale Alert, on Twitter.
This study examines the weak form of the efficient market hypothesis for Bitcoin using a feedforward neural network. Due to the increasing popularity of cryptocurrencies in recent years, the question has arisen, as to whether market inefficiencies could be exploited in Bitcoin. Several studies we refer to here discuss this topic in the context of Bitcoin using either statistical tests or machine learning methods, mostly relying exclusively on data from Bitcoin itself. Results regarding market efficiency vary from study to study. In this study, however, the focus is on applying various asset-related input features in a neural network. The aim is to investigate whether the prediction accuracy improves when adding equity stock indices (S&P 500, Russell 2000), currencies (EURUSD), 10 Year US Treasury Note Yield as well as Gold&Silver producers index (XAU), in addition to using Bitcoin returns as input feature. As expected, the results show that more features lead to higher training performance from 54.6% prediction accuracy with one feature to 61% with six features. On the test set, we observe that with our neural network methodology, adding additional asset classes, no increase in prediction accuracy is achieved. One feature set is able to partially outperform a buy-and-hold strategy, but the performance drops again as soon as another feature is added. This leads us to the partial conclusion that weak market inefficiencies for Bitcoin cannot be detected using neural networks and the given asset classes as input. Therefore, based on this study, we find evidence that the Bitcoin market is efficient in the sense of the efficient market hypothesis during the sample period. We encourage further research in this area, as much depends on the sample period chosen, the input features, the model architecture, and the hyperparameters.
Shivam Kumar Singh, Krishna Pal Sharma, Prashant Kumar
Referring to the recent price boom and bust of cryptocurrencies, Bitcoin is the world's most well-known Cryp-tocurrency, making it enticing to financial market participants. The significant volatility of the Bitcoin conversion scale makes it difficult to predict. As a result, forecasting its behavior is critical for monetary business sectors. However, machine learning based solutions have been proved to be more promising for providing much accurate Bitcoin price predictions. Thus, the aim of this work is to explore and critically analyze the various machine learning based approaches. Further, this work is focused to provide all essential details from the fundamental knowledge of crypto currency particularly Bitcoin to its architecture and price inflation with various factors which is useful for proceeding research and study in the field. The work covers analysis of different supervised machine learning methods assessed to acquire the most pertinent traits for the prediction. The prediction results are also used as inputs to enhance price direction predictions. The outcomes demonstrated that the chosen features and the efficient machine learning and deep learning technique improve accuracy.
Yeni finansal varlıklar ile klasik yatırım enstrümanları arasındaki ilişkilerin önemi artmaktadır. Çalışmada Diks ve Panchenko Doğrusal Olmayan Nedensellik Testi kullanarak Bitcoin, Ethereum fiyatları ve borsa endeksleri arasındaki nedensellik ilişkileri incelenmektedir. Çalışma dönemi covid-19 pandemisinin Türkiye’de ilan edildiği tarihten başlayarak 11/03/2020-06/04/2022 arasındaki günlük frekanstaki verileri kapsamaktadır. Böylece pandemi süreciyle birlikte küresel çaplı krizlerde kripto para birimleri ile küresel finans piyasaları arasındaki ilişkinin incelenmesi hedeflenmiştir. Çalışma literatürde özellikle Ethereum ile ilgili yapılmış çalışmaların kısıtlı olması sebebi ile diğer çalışmalardan ayrılmaktadır. Kullanılan doğrusal olmayan nedensellik analizi sonuçlarına bakıldığında, dünya borsa endeksi ile Bitcoin fiyatı arasında ve Asya borsa endeksi ile Ethereum fiyatı arasında çift yönlü nedensellik ilişkisi olduğu tespit edilmiştir. Ayrıca Bitcoin fiyatına Avrupa ve ABD borsa endekslerinden tek yönlü nedensellik olduğu görülmektedir. Aynı zamanda Bitcoin fiyatından da Asya borsa endeksine tek yönlü nedensellik ilişkisi mevcuttur. Ethereum fiyatından ise Avrupa borsa endeksine doğru tek yönlü nedensellik ilişkisi söz konusudur.
H. M. Tenkam, Jules C. Mba, Sutene Mwambetania Mwambi
This paper focuses on the selection and optimisation of a cryptoasset portfolio, using the K-means clustering algorithm and GARCH C-Vine copula model combined with the differential evolution algorithm. This integrated approach allows the construction of a diversified portfolio of eight cryptocurrencies and determines an optimal allocation strategy making it possible to minimize the conditional value-at-risk of the portfolio and maximise the return. Our results show that stablecoins such as True-USD are negatively correlated to the other cryptoassets in the portfolio and could therefore be a safe haven for crypto-investors during market turmoil. Our findings are in line with previous studies exhibiting stablecoins as potential diversifiers.
This study aims to analyze the causal relationship between electricity consumption, price and transaction volume of Bitcoin, which is the most important asset of the crypto money market in terms of both market capitalization and transaction volume. In this study, the Bitcoin electricity consumption variable is represented by Cambridge Bitcoin Electricity Consumption Index. As the data set, 1446 days of data between February 2017 and February 2021 were used. The causality relationship between the variables is analyzed using the Hatemi-J (2012) and Toda Yamamoto (1995) tests. In addition, this study is a rare study that examines the relationship between electricity and volume, together with the work done by Schinckus et al. (2020). According to the results of this study, the decrease in Bitcoin electricity consumption causes a decrease in the Bitcoin price. However, a negative relationship is detected Bitcoin electricity consumption and Bitcoin trade volume in this study, like the study by Schinckus et al. (2020), the relationship was found to be very weak.
João Tiago Aparício, Mário Rom�ão, Carlos J. Costa
The current study's goal is to explain the price of bitcoins. We examined the effect of Web search statistics, energy prices, and alternative investment (or cost of opportunity) on bitcoin prices in particular. The second goal is to find the algorithm with the best predictive power. Data were obtained from public and open data. We use a variety of machine learning algorithms to accomplish this. Statistical results were coherent according to the expectation.
The author studies the explosive behaviors, causality relationships, and contagion effects between three financial markets using the daily closing prices of Bitcoin, gold, and West Texas Intermediate (WTI) oil prices for a sample period from July 19, 2010 to September 10, 2021. By employing the generalized supremum augmented Dickey-Fuller (GSADF) approach, the author finds significant evidence of bubble explosive behaviors in the Bitcoin and WTI prices—but not in the gold prices—and these periods mostly match with the periods of quantitative easing and financial stress. Besides, the test shows several short and long episodes of unilateral causal linkages from Bitcoin returns to oil price changes under homoscedasticity and heteroskedasticity assumptions. The results show no evidence for the contagion effect of bubbles between cryptocurrency and oil markets during the sample period.
Ruzita Abdul‐Rahim, Airil Khalid, Zulkefly Abdul Karim, Mamunur Rashid
This paper estimates the comovement between two leading cryptocurrencies and the G7 stock markets. It then attempts to explain the comovement with the rational investment theory by examining whether it is driven by market uncertainty measures, public attention to COVID-19, and the government’s containment and health responses to COVID-19. Wavelet Coherence heatmaps show that the stock-cryptocurrency comovements increase significantly and positively during the pandemic, indicating that cryptocurrencies lose their safe haven properties against stocks during the heightened market uncertainties. Over the longer investment horizons, Bitcoin reemerges as a safe haven or strong hedger while Ethereum’s properties weaken. Seemingly Unrelated Regression results reveal that the stock-cryptocurrency comovements are rationally explained by market uncertainties, government responses to COVID-19, and market fundamentals. However, the comovements are also driven by the fear of COVID-19 to a certain extent. Our findings offer valuable insights for investors considering cryptocurrencies to rebalance their equity portfolios during market distress. For policymakers, the Economic Policy Uncertainty (EPU) results suggest that government policies and regulatory frameworks can be used to regulate speculation and investment activities in the cryptocurrency market.