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3,636 results · page 129 of 152
Weiyi Liu, Xuan Liang, Guowei Cui
No abstract is available for this record.
Carol Alexander, Michael Dakos
Less than half the crytocurrency papers published since January 2017 employ correct data
Paraskevi Katsiampa, Shaen Corbet, Brian M. Lucey
Through the application of Diagonal BEKK and Asymmetric Diagonal BEKK methodologies to intra-day data for eight cryptocurrencies, this paper investigates not only conditional volatility dynamics of major cryptocurrencies, but also their volatility co-movements. We first provide evidence that all conditional variances are significantly affected by both previous squared errors and past conditional volatility. It is also shown that both methodologies indicate that cryptocurrency investors pay the most attention to news relating to Neo and the least attention to news relating to Dash, while shocks in OmiseGo persist the least and shocks in Bitcoin persist the most, although all of the considered cryptocurrencies possess high levels of persistence of volatility over time. We also demonstrate that the conditional covariances are significantly affected by both cross-products of past error terms and past conditional covariances, suggesting strong interdependencies between cryptocurrencies. It is also demonstrated that the Asymmetric Diagonal BEKK model is a superior choice of methodology, with our results suggesting significant asymmetric effects of positive and negative shocks in the conditional volatility of the price returns of all of our investigated cryptocurrencies, while the conditional covariances capture asymmetric effects of good and bad news accordingly. Finally, it is shown that time-varying conditional correlations exist, with our selected cryptocurrencies being strongly positively correlated, further highlighting interdependencies within cryptocurrency markets.
Wolfgang Karl Härdle, Campbell R. Harvey, Raphael Constantin Georg Reule
Abstract Cryptocurrency refers to a type of digital asset that uses distributed ledger, or blockchain, technology to enable a secure transaction. Although the technology is widely misunderstood, many central banks are considering launching their own national cryptocurrency. In contrast to most data in financial economics, detailed data on the history of every transaction in the cryptocurrency complex are freely available. Furthermore, empirically oriented research is only now beginning, presenting an extraordinary research opportunity for academia. We provide some insights into the mechanics of cryptocurrencies, describing summary statistics and focusing on potential future research avenues in financial economics.
Samuel Asante Gyamerah
Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. This paper evaluates the volatility of Bitcoin returns using three GARCH models (sGARCH, iGARCH, and tGARCH). The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distribution in the return series of Bitcoin. Comparative to the Students't-distribution and the Generalized error distribution, the Normal Inverse Gaussian (NIG) distribution captured adequately the leptokurtic and skewness in all the GARCH models. The tGARCH model was the best model as it described the asymmetric occurrence of shocks in the Bitcoin market. That is, the response of investors to the same amount of good and bad news are distinct. From the empirical results, it can be concluded that tGARCH-NIG was the best model to estimate the volatility in the return series of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH type models since time series of most cryptocurrency are leptokurtic.
Dehua Shen, Andrew Urquhart, Pengfei Wang
Abstract This paper studies the volatility of Bitcoin and determines the importance of jumps and structural breaks in forecasting volatility. We show the importance of the decomposition of realized variance in the in‐sample regressions using 18 competing heterogeneous autoregressive (HAR) models. In the out‐of‐sample setting, we find that the HARQ‐F‐J model is the superior model, indicating the importance of the temporal variation and squared jump components at different time horizons. We also show that HAR models with structural breaks outperform models without structural breaks across all forecasting horizons. Our results are robust to an alternative jump estimator and estimation method.
Ladislav Krištoufek
Bitcoin as a major cryptocurrency has come up as a shooting star of the 2017 and 2018 headlines. After exploding its price twenty times just in the twelve months of 2017, the tone has changed dramatically in 2018 after major price corrections and increasing concerns about its mining power consumption and overall sustainability. The dynamics and interaction between Bitcoin price and its mining costs have become of major interest. Here we show that these two quantities are tightly interconnected and they tend to a common long-term equilibrium. Mining costs adjust to the cryptocurrency price with the adjustment time of several months up to a year. Current developments suggest that we have arrived at a new era of Bitcoin mining where marginal (electricity) costs and mining efficiency play the prime role. Presented results open new avenues towards interpreting past and predicting future developments of the Bitcoin mining framework.
Sang Hoon Kang, Ron McIver, José Arreola Hernández
In this paper, we use dynamic conditional correlations (DCCs) and wavelet coherence to examine the hedging and diversification properties of gold futures vis-à-vis Bitcoin prices. Our research aims to reveal whether the bubble patterns of behavior in gold futures prices can be used to hedge against the bubble behavior in the Bitcoin market in the short-term, and vice versa; as well as whether each can be used to manage and hedge overall market and sector downside risk of the other asset/commodity. We find evidence of volatility persistence, causality, and phase differences between Bitcoin and gold futures prices. Contagion is observed to increase during the European sovereign debt crisis. Wavelet coherence results indicate a relatively high degree of co-movement across the 8–16 weeks frequency band between Bitcoin and gold futures prices for the 2012–2015 time period.
Aniruddha Dutta, S. Sai Kumar, Meheli Basu
In today’s era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.
Dionisis Philippas, Hatem Rjiba, Khaled Guesmi, Stéphane Goutte
No abstract is available for this record.
George S. Atsalakis, Ioanna G. Atsalaki, Fotios Pasiouras, Constantin Zopounidis
No abstract is available for this record.
Dirk G. Baur, Lai T. Hoang
No abstract is available for this record.
K. Matsuura
Financial Technology (FinTech) is considered a taxonomy that describes a wide range of ICT (information and communications technology) associated with financial transactions and related operations. Improvement of service quality is the main issue addressed in this taxonomy, and there are a large number of emerging technologies including blockchain-based cryptocurrencies and smart contracts. Due to its innovative nature in accounting, blockchain can also be used in lots of other FinTech contexts where token models play an important role for financial engineering. This paper revisits some of the key concepts accumulated behind this trend, and shows a generalized understanding of the technology using an adapted stochastic process. With a focus on financial instruments using blockchain, research directions toward stable applications are identified with the help of a newly proposed stabilizer: interpretation function of token valuation. The idea of adapted stochastic process is essential for the stabilizer, too.
Andrés Solimano
The development of new digital technologies in the areas of cryptography, distributed ledgers and mobile phones is affecting the way money is used for economic transactions. Electronic payments systems are rapidly replacing the use of cash. New powerful distributed ledger technologies, operated on a peer-to-peer decentralized basis is leading to the rapid expansion of digital money, with bitcoin being the most prominent digital currency (although there are more than one-thousand different crypto-currencies).
Roberto B. Corcino, Karl Patrick Casas, Allan Roy Elnar
In this paper, a mathematical model is constructed that would capture the pattern of the MSD of fluctuations of Bitcoin unit prices over time in daily basis by applying the method of White Noise Analysis. The raw data of 2805 ordered points (t,p) are used in the study, which are taken from coindesk.com, where p is the Bitcoin unit price at given time t.
Leopoldo Catania, Stefano Grassi, Francesco Ravazzolo
No abstract is available for this record.
Andrii Bielinskyi, Vladimir Soloviev
This article demonstrates the possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex networks have been used. The possibility of constructing dynamic measures of network complexity behaving in a proper way during actual pre-crash periods has been shown. This fact is used to build predictors of crashes and critical events phenomena on the examples of all the patterns recorded in the time series of the key cryptocurrency Bitcoin, the effectiveness of the proposed indicators-precursors of these falls has been identified.
Sashikanta Khuntia, J. K. Pattanayak
No abstract is available for this record.
Xin Li, Shenghong Li, Chong Xu
No abstract is available for this record.
Shay-Kee Tan, Jennifer Chan, Kok Haur Ng
No abstract is available for this record.
Gang‐Jin Wang, Chi Xie, Danyan Wen, Longfeng Zhao
No abstract is available for this record.
Mustafa Özyeşil
Purpose - This study conducts an analysis to reveal the interaction between Bitcoin and Exchange Rates to find out whether Bitcoin is becoming a substitution for the exchange rates.Methodology - To investigate the mutually interaction between the exchange rates and the Bitcoin, the interaction (relationship) between daily closing price of both exchange rates and Bitcoin was analyzed through the Var model. Thus, it was tried to show the sensitivity of the values of Bitcoin to the changes occured in the exchange rates.Findings - Based on Variance Decomposition analysis, BITCOIN and Euro can be considered as largely external variables and their prices are not significantly affected by USD. An interesting result in this study is that the USD exchange rate was found to be significantly sensitive to the Euro.Conclusion - Findings obtained from analysis show that Bitcoin and Excange Rates have not become an alternative tools for each other yet.
Pavel Ciaian, d’Artis Kancs, Miroslava Rajčániová
This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.
Melanie Swan
No abstract is available for this record.