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3,636 results · page 122 of 152
Dilip B. Madan, Sofie Reyners, Wim Schoutens
No abstract is available for this record.
Khamis Hamed Al‐Yahyaee, Mobeen Ur Rehman, Walid Mensi, Idries Mohammad Wanas Al-Jarrah
No abstract is available for this record.
David Vidal-Tomás, Ana M. Ibáñez, José Emilio Farinós Viñas
Cryptocurrencies have attracted the attention of many investors and policymakers given the increase in popularity of Bitcoin. In this context, we analyse the cryptocurrency market by means of cap-weighted and equally weighted market portfolios that include all the altcoins available for three different periods (2015–2017, 2016–2017 and 2017). By using the most traditional tests of efficiency, we observe three main features of the cryptocurrency market: it is weak-form inefficient due to the behaviour of all the altcoins, it is more inefficient over time, especially in 2017, and the creation of new cryptocurrencies has not significantly changed the efficiency of the market.
Mustafa Özyeşil
The main objective of this study is to examine the mutual interaction between crypto money (coins) types. For this purpose, we investigated the sensitivity existence of any crypto money to changes in other crypto types. Methodology-In this study, to find out whether the interaction (relationship) exists between cryptocurrencies VAR model will be used through daily closing prices of each crypt money. Under the VAR analysis, variance decomposition, impact-response functions analysis will be done and finally, Granger Causality Test will be performed. Findings-According to the results of VAR analysis based on Variance Decomposition, BITCOIN, BT CASH and Tether are largely external variables and their prices are not significantly affected by other crypto currencies. In contrast, the values of Etherum, Lite Coin and QTUM are significantly affected by the changes in the values of other crypto coins. Conclusion-In accordance with findings obtained from analysis, we observed that Tether is moving towards becoming an alternative investment tool for all the crypto moneys. Other crypto coins tend to move in the same direction.
José Antonio Núñez Mora, Mario Iván Contreras-Valdez, Carlos A. Franco-Ruiz
This paper develops the ability of the normal inverse Gaussian distribution (NIG) to fit the returns of bitcoin (BTC). As the first cryptocurrency created, the behavior of this new asset is characterized by great volatility. The lack of a proper definition or classification under existing theory exacerbates this property in such a way that explosive periods followed by a rapid decline have been observed along the series, meaning bubble episodes. By detecting the periods in which a bubble rises and collapses, it is possible to study the statistical properties of such segments. In particular, adjusting a theoretical distribution may help to determine better strategies to hedge against these episodes. The NIG is an appropriate candidate not only because of its heavy-tailed property but also because it has been proven to be closed under convolution, a characteristic that can be implemented to measure multivariate value at risk. Using data on the price of BTC with respect to seven of the main global currencies, the NIG was able to fit every time segment despite the bubble behavior. In the out-of-sample tests, the NIG was proven to have an adjustment similar to that of a generalized hyperbolic (GH) distribution. This result could serve as a starting point for future studies regarding the statistical properties of cryptocurrencies as well as their multivariate distributions.
Marc Gillaizeau, Ranadeva Jayasekera, Ahmad Maaitah, Tapas Mishra · 6 authors
No abstract is available for this record.
Johannes Bleher, Thomas Dimpfl
No abstract is available for this record.
Stefano Bistarelli, Alessandra Cretarola, Gianna Figà‐Talamanca, Marco Patacca
No abstract is available for this record.
Paul Gatabazi, J.C. Mba, Edson Pindza, Coenraad C.A. Labuschagne
No abstract is available for this record.
Aviral Kumar Tiwari, Satish Kumar, Rajesh Pathak
We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.
Keshab Shrestha
Abstract We revisit the issue of market efficiency of Bitcoin, which is an important part of the new financial technology (FinTech), by analyzing the Bitcoin returns using two recently developed analytical techniques called bipower variation method and Multifractal Detrended Fluctuation Analysis (MF‐DFA). MF‐DFA allows us to analyze the return series in ways not possible using a monofractal analytical techniques such as detrended fluctuation analysis (DFA) and R/S method. The bipower variation method suggests that the Bitcoin returns are efficient and contain some large finite jumps. Using MF‐DFA, we find that the Bitcoin returns are multifractal and, therefore, the Bitcoin market is not efficient. By carrying out further analysis, we also find that the multifractility and inefficiency are caused by the autocorrelated returns as well as extreme returns.
Maurice Omane‐Adjepong, Paul Alagidede
No abstract is available for this record.
Canh Phuc Nguyen, Udomsak Wongchoti, Thanh Dinh Su, Thong Trung Nguyen
No abstract is available for this record.
Ahmed S. Baig, Benjamin M. Blau, Nasim Sabah
No abstract is available for this record.
Daniel Broby, Devraj Basu, Ashwin Arulselvan
This paper investigates the importance of "time of execution" and the relevance of "precision time" in order driven transactions done over distributed ledgers. We created a distributed marketplace using stock market price data from the Toronto Stock Exchange (TMX). We then proceeded to test and measure the impact of timing of orders at the nanosecond level. Whilst price discovery in order driven markets is done instantaneously, with distributed markets, it is necessary to know which order to process first to avoid "front-running". We argue that a protocol for the time of order of receipt and execution should be subject to nanosecond stacking. Our approach incorporates both transitory and permanent price discovery components. It allows for the efficient processing of transactions and the order that are received by a market clearing distributed ledger.
Tomaso Aste
We study the dependency and causality structure of the cryptocurrency market investigating collective movements of both prices and social sentiment related to almost two thousand cryptocurrencies traded during the first six months of 2018. This is the first study of the whole cryptocurrency market structure. It introduces several rigorous innovative methodologies applicable to this and to several other complex systems where a large number of variables interact in a non-linear way, which is a distinctive feature of the digital economy. The analysis of the dependency structure reveals that prices are significantly correlated with sentiment. The major, most capitalised cryptocurrencies, such as bitcoin, have a central role in the price correlation network but only a marginal role in the sentiment network and in the network describing the interactions between the two. The study of the causality structure reveals a causality network that is consistently related with the correlation structures and shows that both prices cause sentiment and sentiment cause prices across currencies with the latter being stronger in size but smaller in number of significative interactions. Overall our study uncovers a complex and rich structure of interrelations where prices and sentiment influence each other both instantaneously and with lead-lag causal relations. A major finding is that minor currencies, with small capitalisation, play a crucial role in shaping the overall dependency and causality structure. Despite the high level of noise and the short time-series we verified that these networks are significant with all links statistically validated and with a structural organisation consistently reproduced across all networks.
Deepa Pavithran, Jamal N. Al‐Karaki, Rajesh Thomas, Charles Shibu · 5 authors
Bitcoin scheme powered by the blockchain technology is a global payment system that posed great challenges and opportunities for economists, entrepreneurs and consumers. As Bitcoin is still considered to be mysterious and not well understood by many stakeholders, it is essential to analyze the capabilities of Bitcoin and its underlying protocol with regard to different financial aspects. Some of these aspects include Bitcoin ownership, Bitcoin saving trends, transaction authenticity, price stability, and energy consumption. Through this paper we present a data driven analysis of price fluctuations, user behavior, and wealth accumulation in Bitcoin transaction. Of particular interest to stakeholders is the understanding of factors that impact Bitcoin price and the volatility over time. We focus on comparison of Bitcoin price trends vs number of users and the number of transactions. We also analyze the richest addresses and the percentage of wealth in these addresses. Important conclusions are finally summarized.
T. Czapliński, Elena Nazmutdinova
This paper fits in the trend of discussing the efficiency of cryptocurrency markets. Since 2008, when Bitcoin appeared on the market, arbitrageurs from all over the world have been trying to find the gaps in the markets, which will let them earn risk-free money using financial operations. Although a lot of researchers are trying to figure out arbitrage opportunities, looking at different exchanges and using different cryptocurrencies, so far hardly anyone has looked at arbitrage opportunities with the use of FIAT currencies within the same or different exchanges. This paper examines such opportunities for three different exchanges, i.e. Kraken, Bitfinex and Bitstamp -exchanges that enable trading in USD and EUR against Bitcoin at the same time. The main empirical results suggest that there are significant arbitrage opportunities on these markets. In the paper, we also show the main constraints in FIAT currencies arbitrage on cryptocurrency exchanges.
Walid Mensi, Mobeen Ur Rehman, Khamis Hamed Al‐Yahyaee, Idries Mohammad Wanas Al-Jarrah · 5 authors
No abstract is available for this record.
Wang Yiying, Zang Yeze
Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and mechant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that infiuence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyse the price dynamics of Bitcoin, Etherum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilise useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This study provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model.
Tetsuya Takaishi, Takanori Adachi
This letter investigates the dynamic relationship between market efficiency, liquidity, and multifractality of Bitcoin. We find that before 2013 liquidity is low and the Hurst exponent is less than 0.5, indicating that the Bitcoin time series is anti-persistent. After 2013, as liquidity increased, the Hurst exponent rose to approximately 0.5, improving market efficiency. For several periods, however, the Hurst exponent was found to be significantly less than 0.5, making the time series anti-persistent during those periods. We also investigate the multifractal degree of the Bitcoin time series using the generalized Hurst exponent and find that the multifractal degree is related to market efficiency in a non-linear manner.
Avinash Barnwal, Hari Pad Bharti, Aasim Ali, Vishal Krishna Singh
Predicting the direction of assets have been an active area of study and a difficult task. Machine learning models have been used to build robust models to model the above task. Ensemble methods is one of them showing results better than a single supervised method. In this paper, we have used generative and discriminative classifiers to create the stack, particularly 3 generative and 6 discriminative classifiers and optimized over one-layer Neural Network to model the direction of price cryptocurrencies. Features used are technical indicators used are not limited to trend, momentum, volume, volatility indicators, and sentiment analysis has also been used to gain useful insight combined with the above features. For Cross-validation, Purged Walk forward cross-validation has been used. In terms of accuracy, we have done a comparative analysis of the performance of Ensemble method with Stacking and Ensemble method with blending. We have also developed a methodology for combined features importance for the stacked model. Important indicators are also identified based on feature importance.
Nikolay Miller, Yiming Yang, Bruce Sun, Guoyi Zhang
This research studies automatic price pattern search procedure for bitcoin cryptocurrency based on 1-min price data. To achieve this, search algorithm is proposed based on nonparametric regression method of smoothing splines. We investigate some well-known technical analysis patterns and construct algorithmic trading strategy to evaluate the effectiveness of the patterns. We found that method of smoothing splines for identifying the technical analysis patterns and that strategies based on certain technical analysis patterns yield returns that significantly exceed results of unconditional trading strategies.
Ayse Metin KarakaÅŸ
The application of entropy in finance can be regarded as the extension of information entropy and probability theory. In this article we apply the concept of entropy for basic crypto money (Ethereum and Bitcoin) to make a comparison. We compute in the first step Shannon entropy with different estimators, Tsallis entropy for different values of its parameter, Rényi entropy and at last the approximate entropy. We provide computational results for these entropies for daily data.