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4,843 results · page 197 of 202
Yaohao Peng, Pedro Henrique Melo Albuquerque, Jader Martins Camboim de Sá, ANA JULIA AKAISHI PADULA · 5 authors
No abstract is available for this record.
Huisu Jang, Jaewook Lee
Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin's supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.
Pradipta Kumar Sahoo
This paper examines the comprehensive idea about the growth and future sustainability of bitcoin as a cryptocurrency. The transaction volume of bitcoin is used as the growth of the bitcoin and the bitcoin log return is used for testing the volatility which is helpful for the future sustainability of bitcoin. The study period says that the growth of bitcoin’s transaction volume is an increasing trend as more day to day transaction is minting with the exchange of Bitcoin. The study also uses ARCH & GARCH methodology to know the volatility of this emerging digital currency, and the GARCH result shows that it is a highly volatile currency. As a result, most of the governments have not given their legal status for the use of bitcoin in their country. But if bitcoin will be stable in the future, then it is easily accepted through worldwide and in the long run, people will have more faith in the cryptocurrency technology and its usability.
Josef Kurka
Large stream of literature studies interconnectedness among various assets that are relevant in current global markets. Transmission of shocks between cryptocurrencies and traditional asset classes is, however, not understood at all, but should not be ignored due to increasing influence of cryptocurrencies in recent years. In this paper, we study how shocks between the most liquid representatives of the traditional asset classes including commodities, foreign exchange, stocks, financials, and cryptocurrencies are being transmitted. Generally, we document very low level of connectedness between the main cryptocurrency and other studied assets. The only exception is gold which receives substantial amount of shocks from cryptocurrency market. Our findings are important since we show that cryptocurrencies play role in global markets, and the results could also be useful in portfolio diversification schemes. Moreover, we find significant positive asymmetry in spillovers between the studied assets, which is in contradiction to previous studies conducted on assets from a single asset class.
Premkumar Chithaluru, Kulvinder Singh, Manish Sharma
As the technologies are evolving day by day, they are able to rejuvenate any sector either individually or by incorporating other technologies. There are many prominent sectors in the market such as healthcare, education, entertainment, business, information technology, retail, etc. Every sector has its own set of profits and consequences, but apart from all, the banking or finance sector is the only sector that provides dynamicity to all other sectors and helps them to generate maximum revenue from their principal investment. In this chapter, the authors are focusing on the traditional and modern ways of banking, currencies such as cryptocurrency like Bitcoin, Ethereum, Litecoin, and how the modern currency will change the transaction procedure in the global banking system, creating an amalgamation of such currency with a current transaction system with the role of technology such as Blockchain in the betterment of the global banking system making the system fully decentralized, distributed, transparent, fast, immutable, and efficient.
Pedro Jorge Melgo Vieira
No abstract is available for this record.
Salim Lahmiri, Stelios Bekiros
No abstract is available for this record.
Ross C. Phillips, Denise Gorse
Financial price bubbles have previously been linked with the epidemic-like spread of an investment idea; such bubbles are commonly seen in cryptocurrency prices. This paper aims to predict such bubbles for a number of cryptocurrencies using a hidden Markov model previously utilised to detect influenza epidemic outbreaks, based in this case on the behaviour of novel online social media indicators. To validate the methodology further, a trading strategy is built and tested on historical data. The resulting trading strategy outperforms a buy and hold strategy. The work demonstrates both the broader utility of epidemic-detecting hidden Markov models in the identification of bubble-like behaviour in time series, and that social media can provide valuable predictive information pertaining to cryptocurrency price movements.
Dirk G. Baur, Thomas Dimpfl, Konstantin Kuck
No abstract is available for this record.
Asani Sarkar
No abstract is available for this record.
Nhi N.Y. Vo, Guandong Xu
The 2008 financial crisis had scattered incredulity around the globe regarding traditional financial systems, which made investors and non-financial customers turn to other alternative such as digital banking systems. The existence and development of blockchain technology make cryptocurrency in recent years believably become a complete alternative to traditional ones. Bitcoin is the world's first peer-to-peer and decentralized digital cash system initiated by Nakamoto [1]. Though being the most prominent cryptocurrency, Bitcoin has not been a legal trading currency in various countries. Its exchange rate has appeared to be an exceptionally high-risk portfolio with extreme volatility, which requires a more detailed evaluation before making any decision. This paper utilizes knowledge of statistics for financial time series and machine learning to (i) fit the parametric distribution and (ii) model and forecast the volatility of Bitcoin returns, and (iii) analyze its correlation to other financial market indicators. The fitted parametric time series model significantly outperforms other standard models in explaining the stylized facts and statistical variances in the behavior of Bitcoin returns. The model forecast also outperforms some machine learning methodologies, which would benefit policy makers, banks and financial investors in trading activities for both long-term and short-term strategies.
Jeffrey Chu, Stephen Chan, Saralees Nadarajah, Joerg Osterrieder
With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates.
Aurelio F. Bariviera
This letter revisits the informational efficiency of the Bitcoin market. In particular we analyze the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent. Our results are twofold. First, R/S method is prone to detect long memory, whereas DFA method can discriminate more precisely variations in informational efficiency across time. Second, daily returns exhibit persistent behavior in the first half of the period under study, whereas its behavior is more informational efficient since 2014. Finally, price volatility, measured as the logarithmic difference between intraday high and low prices exhibits long memory during all the period. This reflects a different underlying dynamic process generating the prices and volatility.
Hermann Elendner, Simon Trimborn, Bobby Ong, Teik Ming Lee
No abstract is available for this record.
Pedro Bonillo Bueno, Emilio Aragon Fortes, Konstantinos Vlachoski
Since its launch in 2008, Bitcoin becomes one of the most successful and fast-growing alternative currencies. As of 2017, the market capitalization is around $46 billion and arguably expected to continue growing. The Bitcoin to the US dollar exchange rate has been very volatile and fluctuating significantly. Although Bitcoin was designed as a medium of exchange, it is now more as an investment tool and thus the development of effective quantitative risk management tools becomes quite urgent for all the market participants. In this paper, we investigate empirical distribution of the Bitcoin exchange rate returns by using four types of widelyused heavy-tailed distribution and show that the Skewed t distribution has the best empirical performance. We further calculate the VaR based risk measures and found the Skewed t distribution generates the VaR values, which are closest to historical VaR values. Our results could be directly used in the industry’s stress testing practice, and help financial institutions fulfill the regulatory requirements.
Thomas Kim
No abstract is available for this record.
Jamal Bouoiyour, Refk Selmi
Although Bitcoin has long been dominant in the crypto scene, it is certainly not alone. Ether is another cryptocurrency related project that has attracted an intensive attention because of its additional features. This study seeks to test whether these cryptocurrencies differ in terms of their volatile and speculative behaviors, hedge, safe haven and risk diversification properties. Using different econometric techniques, we show that a) Bitcoin and Ether are volatile and relatively more responsive to bad news, but the volatility of Ether is more persistent than that of Bitcoin; b) for both cryptocurrencies, the exuberance and the collapse of bubbles were identified, but Bitcoin appears more speculative than Ether; c) there is negative and significant correlation between Bitcoin/Ether and other assets (S\&P500 stocks, US bonds, oil), which would indicate that digital currencies can hedge against the price movements of these assets; d) there is negative tail independence between Bitcoin/Ether and other financial assets, implying that these cryptocurrencies exhibit the function of a weak safe haven; and e) The inclusion of Bitcoin/ Ether in a portfolio improve its efficiency in terms of higher reward-to-risk ratios. But investors who hold diversified portfolios made of stocks or bonds and Ether may face losses over bearish regime. In such situation, stock and bond investors may take a short position on Bitcoin.
Tetsuya Takaishi
Using 1-min returns of Bitcoin prices, we investigate statistical properties and multifractality of a Bitcoin time series. We find that the 1-min return distribution is fat-tailed, and kurtosis largely deviates from the Gaussian expectation. Although for large sampling periods, kurtosis is anticipated to approach the Gaussian expectation, we find that convergence to that is very slow. Skewness is found to be negative at time scales shorter than one day and becomes consistent with zero at time scales longer than about one week. We also investigate daily volatility-asymmetry by using GARCH, GJR, and RGARCH models, and find no evidence of it. On exploring multifractality using multifractal detrended fluctuation analysis, we find that the Bitcoin time series exhibits multifractality. The sources of multifractality are investigated, confirming that both temporal correlation and the fat-tailed distribution contribute to it. The influence of "Brexit" on June 23, 2016 to GBP--USD exchange rate and Bitcoin is examined in multifractal properties. We find that, while Brexit influenced the GBP--USD exchange rate, Bitcoin was robust to Brexit.
Mariusz Tarnopolski
The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst\nexponent $H>0.5$, is exploited in order to predict future BTC/USD price. A\nMonte Carlo simulation with $10^4$ geometric fractional Brownian motion\nrealisations is performed as extensions of historical data. The accuracy of\nstatistical inferences is 10\\%. The most probable Bitcoin price at the\nbeginning of 2018 is 6358 USD.\n
Pavel Ciaian, Miroslava Rajčániová, d’Artis Kancs
This paper empirically examines interdependencies between BitCoin and altcoin markets in the short- and long-run. We apply time-series analytical mechanisms to daily data of 17 virtual currencies (BitCoin + 16 alternative virtual currencies) and two altcoin price indices for the period 2013–2016. Our empirical findings confirm that indeed BitCoin and altcoin markets are interdependent. The BitCoin-altcoin price relationship is significantly stronger in the short-run than in the long-run. We cannot fully confirm the hypothesis that the BitCoin price relationship is stronger with those altcoins that are more similar in their price formation mechanism to BitCoin. In the long-run, macro-financial indicators determine the altcoin price formation to a slightly greater degree than BitCoin does. The virtual currency supply is exogenous and therefore plays only a limited role in the price formation.
Paraskevi Katsiampa
No abstract is available for this record.
Fahad Almudhaf
This article examines the pricing efficiency of Bitcoin Investment Trust. We investigate the deviation between prices and net asset values and find that there is a significant and persistent premium with an average of 44%. Such evidence points to pricing inefficiency of the currently available trust and encourages practitioners to introduce better instruments such as Exchange Traded Funds as alternatives to investors interested in having exposure to bitcoins and the digital currencies market.
Obryan Poyser
Currently, there is no consensus on the real properties of Bitcoin. The\ndiscussion comprises its use as a speculative or safe haven assets, while other\nauthors argue that the augmented attractiveness could end accomplishing money's\nfunctions that economic theory demands. This paper explores the association\nbetween Bitcoin's market price and a set of internal and external factors using\nBayesian Structural Time Series Approach. I aim to contribute to the discussion\nby differentiating among several attractiveness sources and employing a method\nthat provides a more flexible analytic framework that decompose each of the\ncomponents of the time series, apply variable selection, include information on\nprevious studies, and dynamically examine the behavior of the explanatory\nvariables, all in a transparent and tractable setting. The results show that\nthe Bitcoin price is negatively associated with a neutral investor's sentiment,\ngold's price and Yuan to USD exchange rate, while positively related to stock\nmarket index, USD to Euro exchange rate and variated signs among the different\ncountries' search trends. Hence, I find that Bitcoin has mixed properties since\nstill seems to act as a speculative, safe haven and a potential a capital\nflights instrument.\n
Elie Bouri, Rangan Gupta, Chi Keung Marco Lau, David Roubaud · 5 authors
We apply different techniques and uncover the quantile conditional dependence between the global financial stress index and Bitcoin returns from July 18, 2010, to December 29, 2017. The results from the copula-based dependence show evidence of right-tail dependence between the global financial stress index and Bitcoin returns. We focus on the conditional quantile dependence and indicate that the global financial stress index strongly Granger-causes Bitcoin returns at the left and right tail of the distribution of the Bitcoin returns, conditional on the global financial stress index. Finally, we use a bivariate cross-quantilogram approach and show only limited directional predictability from the global financial stress index to Bitcoin returns in the medium term, for which Bitcoin can act as a safe-haven against global financial stress.