Explaining changes in bitcoin's price and predicting its future have been the foci of many research studies. In contrast, far less attention has been paid to the relationship between bitcoin's mining costs and its price. One popular notion is the cost of bitcoin creation provides a support level below which this cryptocurrency's price should never fall because if it did, mining would become unprofitable and threaten the maintenance of bitcoin's public ledger. Other research has used mining costs to explain or forecast bitcoin's price movements. Competing econometric analyses have debunked this idea, showing that changes in mining costs follow changes in bitcoin's price rather than preceding them, but the reason for this behavior remains unexplained in these analyses. This research aims to employ economic theory to explain why econometric studies have failed to predict bitcoin prices and why mining costs follow movements in bitcoin prices rather than precede them. We do so by explaining the chain of causality connecting a bitcoin's price to its mining costs.
This research aims to evaluate whether dynamic portfolios consisting of bitcoin and LQ45 stocks outperform portfolios composed solely of LQ45 stocks, especially during the Covid-19 pandemic. Accordingly, we use the time-series data of eight stocks and bitcoin from January 1, 2020, to December 31, 2020. We then run the DCC-GARCH method to analyze better the dynamic correlation between assets and the abnormalities of stock return distributions. The findings demonstrate that bitcoin is negatively correlated with LQ45 stocks, and hence, it can be used to hedge against stock assets. Further, we measure the portfolio performance of bitcoin-hedged and unhedged stock portfolios using the Jensen Index, Treynor Index, Sharpe Index, Sortino Ratio, and Omega Ratio. These measures consistently indicate that bitcoin-hedged stocks outperform unhedged stocks. In sum, our study concludes that incorporating bitcoin into portfolio formation improves portfolio performance.
Tüm dünyayı etkisi altına alan merkeziyetsiz finans oluşumları ve yaşanan dönüşüm günümüzde birçok kişi tarafından ilgiyle karşılanmaktadır. Arkasında barındırdığı teknolojinin yenilikçi ve işlevsel olması, kripto paraların market hacimlerinin günden güne artması ve değerlenmesi, yatırımcıları bu alana çeken yegane faktörlerden bir tanesidir. Ticari alım satım işlemlerinin sağlanabilmesi için sadece bir adet akıllı cihaz ve internete ihtiyaç duyulması, ulaşılabilirlik konusunun da bir hayli kolay olmasını sağlamaktadır. Yoğunluklu olarak son dönemlerde finans dünyasının içindeki büyük otoritelere karşı bir baş kaldırı hareketi olarak adlandırılan bu dönüşüm, arkasında milyonlarca bireysel ve kurumsal yatırımcıyı barındırmaktadır. Bu çalışmada, Bitcoin ve hisse senedi piyasaları arasındaki karşılıklı ilişki incelenmiştir. Dünya üzerinde bulunan seçkin borsa endeksleri çalışmada kendisine yer bulmuştur. Çalışmada 01 Ocak 2013 - 31 Ekim 2021 yılları arasında haftalık veriler kullanılmış, VAR analizi yardımı ve Granger Nedensellik Testi aracılığı ile değişkenler arasındaki ilişki test edilmiştir. Analizden elde edilen bulgulara göre Granger nedensellik analizi sonuçlarına göre Bitcoin değişkeninden Dow Jones endeksine %5 anlamlılık düzeyinde tek yönlü bir ilişki olduğu tespit edilmiştir. Analizden elde edilen bir diğer bulgu ise %10 anlamlılık düzeyinde Bitcoin değişkeninden S&P500 endeksine tek yönlü bir nedensellik ilişkisi olduğu saptanmıştır.
Mario Iván Contreras-Valdez, José Antonio Núñez Mora, Guillermo Benavides Perales
This study presents a multivariate study regarding Bitcoin and its interactions with other financial assets of different classes. This is done by adjusting a multivariate semi heavy-tailed distribution to portfolios containing indexes, currencies, and commodities and one cryptocurrency. Later, a rolling window is deployed to obtain the dynamic parameters of the distribution in a weekly basis. With a Markowitz specification problem, the optimal portfolio weights are computed dynamically using the parameters of the multivariate NIG distribution as inputs. The results provide evidence that correlations of Bitcoin with other assets may provide certain degree of diversification to portfolios; nevertheless, the high volatility of this asset makes it unpractical to employ in significant weights. This paper is relevant for researchers and practitioners as it provides a new tool to manage portfolios with cryptocurrencies and more reliable weights to the asset allocation.
Mamoona Zahid, Farhat Iqbal, Abdul Raziq, Naveed Sheikh
Using the high-frequency data of Bitcoin, this study aims to model the time-varying volatility identified in the residuals of the heterogeneous autoregressive (HAR) model of realized volatility using the symmetric, asymmetric and long-memory generalized autoregressive conditional heteroscedastic models (GARCH) models. We further extended these models by incorporating jumps and continuous components in the realized volatility estimators and investigating the impact of the inverse leverage effect. The Diebold Mariano and model confidence set test confirm that the forecasting performance of HAR-type models can be effectively improved by these innovations. The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized volatility models. The findings of this study may benefit individual investors and risk managers who wish to minimize risks and diversify their portfolios to maximize profits in Bitcoin’s investment.
Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price movement of this highly volatile asset. While fitting the Gaussian and the Generalized Hyperbolic and the Normal Inverse Gaussian (NIG) distributions to log-returns of Bitcoin, NIG distribution appears to provide the best fit. The time-varying Hurst parameter for Bitcoin price reveals periods of randomness and mean-reverting type of behaviour, motivating the study in this paper through fractional Ornstein–Uhlenbeck driven by a Normal Inverse Gaussian Lévy process. Features such as long-range memory are jump diffusion processes that are well captured with this model. The results present a 95% prediction for the price of Bitcoin for some specific dates. This study contributes to the literature of Bitcoin price forecasts that are useful for Bitcoin options traders.
The main purpose of this study is to reveal whether seasonal/time-oriented/calendar anomalies affect the price and transaction volume of Bitcoin. Day of the week and month of the year anomalies are examined in this context. The data for the years 2013-2021 are handled in 3 different sampling periods, consisting of the whole of this time period and each of its divided parts. The existence of these anomalies is analyzed with EGARCH models created separately. The most important conclusion reached in this study is that the analyzed anomalies differ according to the sampling periods. The common findings reached as a result of the analyzes for all three time intervals are as follows: It has been determined that Monday has positive effects in terms of both Bitcoin return and transaction volume, while Saturday has negative effects only regarding transaction volume. Mondays, Tuesdays, and Wednesdays create volatility-increasing effects concerning returns, Friday, Saturday and Sunday reduce volatility. In terms of trading volume, Monday and Tuesday reduce volatility, while Thursday and Friday increase volatility. Whereas March has a positive effect on return volatility, it has a negative effect on trading volume volatility, and September has only a negative effect on return volatility.
In this paper, we investigate the role of Bitcoin as a safe haven against the stock market losses during the spread of COVID-19. The performed analysis was based on a regression model with dummy variables defined around some crucial dates of the pandemic and on the dynamic conditional correlations. To try to model the real dynamics of the markets, we studied the safe-haven properties of Bitcoin against thirteen of the major stock market indexes losses using daily data spanning from 1 July 2019 until 20 February 2021. A similar analysis was also performed for Ether. Results show that this pandemic impacts on the Bitcoin status as safe haven, but we are still far from being able to define Bitcoin as a safe haven.
Renan Gomes Mendes Diniz, Diogo de Prince, Leandro Maciel
Purpose The aim of this paper is to test the existence of bubbles for the daily prices of cryptocurrencies Bitcoin and Ethereum and verify if there is a relationship between bubbles and volatility regimes. Design/methodology/approach The authors test the presence of bubbles with the generalized supremum augmented Dickey–Fuller (GSADF) test using critical values simulated by the bootstrap procedures of Gutierrez (2011), Harvey et al. (2016) and Pedersen and Schütte (2020). Also, the authors estimate Markov regime switching generalized autoregressive conditional heteroskedasticity model for these cryptocurrencies. Findings The GSADF test result indicates the presence of bubbles for both cryptocurrencies. Simulating critical values by wild-bootstrap, which is robust to non-stationary volatility, leads to the highest number of bubbles in both cryptocurrencies. In addition, based on the estimates of conditional variance models with regime changes, the authors find that the bubbles identified are associated with a regime of low returns volatility, indicating a change in the trade-off between risk and return when the prices of cryptocurrencies differ from their fundamental values. Originality/value To the best of the authors knowledge, there are no studies that test the explosive behavior for cryptocurrencies by the GSADF test using the bootstrap method to simulate critical values from the procedures of Harvey et al. (2016) or Pedersen and Schütte (2020). These bootstrapping procedures are robust to heteroscedasticity and avoid the detection of false bubbles. Further, the advantage of Harvey et al. (2016) procedure is the robustness to non-stationary volatility.
Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.
In the research, the causal relationships between Bitcoin, gold and oil prices were examined. The data of the research covers the period from 2015 to July 2020 and consists of daily price values. Augmented Dickey-Fuller Unit Root Test was used to see whether the stochastic process changes with time. Bitcoin and gold series do not contain a unit root since the oil series is stationary at the level while the difference is stationary. The reason why the series containing unit roots are not stationary is due to structural breaks or not, was investigated by Bai-Perron Unit Root Test with Multiple Structural Breaks. According to the test, it was determined that the Bitcoin series has one break and two regimes, while the gold series has two structural breaks and three different regimes. Whether the research series are cointegrated or not was investigated with the Gregory and Hansen test. The causality between the series was examined with the Toda-Yamamoto causality test, which is based on the VAR (Vector Autoregression) model and examines the causality in the series regardless of the unit root. A two-way causality relationship was determined between the eight lag-long Gold series and the Bitcoin series. In other cases, a causal relationship has not been established. As a result, we give an evidence that Bitcoin and gold prices series followed a parallel pattern while with oil not. Therefore, investors can add Bitcoin into their portfolios to make balance of the risk and return.
The last few years have seen a paradigm shift in the financial sector with the development of cryptocurrencies as an alternative mode of payment as well as an investment scheme. The aim of this study is two-fold. The first is to quantify the volatility of cryptocurrencies in terms of the dynamics of tail-end behavior using different approaches and choose the one with the lowest value-at-risk. The second is to investigate the effect of its inclusion in a portfolio with and without gold, to see if Bitcoin is indeed the “digital gold”. This paper uses the generalized simulated annealing optimization technique to compare portfolios for ten countries across the world. The data provide convincing evidence in favor of the inclusion of Bitcoin in the optimized portfolios. Rolling-window analyses (three-year and five-year) confirm the same. However, for some countries, the empirical pattern suggests that instead of replacing gold from the portfolio, both should be comprised. Our results are robust in terms of the inclusion of non-linear constraints.
The success of Bitcoin has spurred emergence of countless alternative coins with some of them shutting down only few weeks after their inception, thus disappearing with millions of dollars collected from enthusiast investors through initial coin offering (ICO) process. This has led investors from the general population to the institutional ones, to become skeptical in venturing in the cryptocurrency market, adding to its highly volatile characteristic. It is then of vital interest to investigate the life span of available coins and tokens, and to evaluate their level of survivability. This will make investors more knowledgeable and hence build their confidence in hazarding in the cryptocurrency market. Survival analysis approach is well suited to provide the needed information. In this study, we discuss the survival outcomes of coins and tokens from the first release of a cryptocurrency in 2009. Non-parametric methods of time-to-event analysis namely Aalen Additive Hazards Model (AAHM) trough counting and martingale processes, Cox Proportional Hazard Model (CPHM) are based on six covariates of interest. Proportional hazards assumption (PHA) is checked by assessing the Kaplan-Meier estimates of survival functions at the levels of each covariate. The results in different regression models display significant and non-significant covariates, relative risks and standard errors. Among the results, it was found that cryptocurrencies under standalone blockchain were at a relatively higher risk of collapsing. It was also found that the 2013–2017 cryptocurrencies release was at a high risk as compared to 2009–2013 release and that cryptocurrencies for which headquarters are known had the relatively better survival outcomes. This provides clear indicators to watch out for while selecting the coins or tokens in which to invest.
We examine the presence of outliers and time-varying jumps in the returns of four major cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin, Litecoin), and a broad cryptocurrency index (CCI30). The results indicate that only Bitcoin returns are contaminated with outliers. Time-varying jumps are present in Bitcoin, Litecoin, Ripple, and the cryptocurrency index. Notably, the presence of jumps in Bitcoin is significant after correcting for outliers. The main findings point to a price instability in some major cryptocurrencies and thereby the importance of accounting for large shocks and time-varying jumps in modelling volatility in the debatable cryptocurrency markets.
The cryptocurrencies are a hot topic in the global financial system. Cryptocurrency is a digital or virtual or internet currency that uses cryptography for security. Cryptocurrency has created unmatched changes in the financial market having both positive and negative contributions. The concept of cryptocurrency is a little hard to accept, but it is easy to use. It is considered difficult because it is entirely different from our conventional currencies that we people are using since ages. Here, we focus the different types of cryptocurrencies, origin and evolution of the term. The role of cryptography in early cryptocurrencies, Issues currently associated with the term, the role of cryptography in today’s cryptocurrencies, cryptocurrencies exchanges, Cryptocurrencies Trading, advantages and disadvantages of cryptocurrencies trading, How Many cryptocurrencies are there? Market Capitalization of Cryptocurrency, the 2021 Global Crypto Adoption Index Top 20, One Year change in the value of Crypto Assets.
This paper examines the dynamic conditional correlations among 10 cryptocurrencies and the possibility of hedging investment strategies among multiple cryptocurrencies over the period affected by COVID-19 from 2017 to 2022. After studying the relationship between Bitcoin, Ethereum, and the other eight cryptocurrencies, four main results were obtained in this paper: first, from the pre-COVID-19 period to the COVID-19 period, almost all of the cryptocurrencies’ return growth rates increased, and COVID-19 had a positive effect on the returns of cryptocurrencies. Second, all of the cryptocurrencies’ return indices had features of volatility clustering and memory persistence in the long run; from pre-COVID-19 to COVID-19, these cryptocurrencies’ GARCH values decreased, but the correlations among the varying GARCH values increased. Third, the varying correlations between the return indices of Bitcoin, Ethereum, and the other cryptocurrencies were very strong; from pre-COVID-19 to COVID-19, the average dynamic correlations between Bitcoin and the others increased. Fourth, Tether can be used as a hedge cryptocurrency against the other cryptocurrencies as COVID-19 enhanced its hedging feature.
The paper proposes a trading strategy for investing in the cryptocurrency market that uses instant market entries based on additional sources of information in the form of a developed dataset. The task of predicting the moment of entering the market is formulated as the task of classifying the trend in the value of cryptocurrencies. To solve it, ensemble models and deep neural networks were used in the present paper, which made it possible to obtain a forecast with high accuracy. Computer analysis of various investment strategies has shown a significant advantage of the proposed investment model over traditional machine learning methods.