Follow blockchain research across journals, conferences, and preprint repositories.
4,843 results · page 179 of 202
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.
Betty Jepkemei, Andrew Kipkebut
Blockchain as technology promises to be a hugely disruptive and empowering technology both in public and private finance applications. As a method to order transactions in a distributed ledger, blockchains offer a record of consensus with a cryptographic audit trail that can be maintained and validated by multiple nodes. It lets contracting parties dynamically track assets and agreements using a common protocol, thus streamlining and even completely collapsing many in-house and third-party verification processes. Block chain originally conceived as the basis of cryptocurrencies, aspects of blockchain technology have far-reaching potential in finance. Although it promises a secure distributed framework to facilitate sharing, exchanging, and the integration of information across all users and third parties, it is important for stakeholders to analyze it in depth for its suitability in business applications. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management among others ,however there is no comprehensive survey on the blockchain as disruptive technology in finance and its application, To fill this gap, we conduct a comprehensive survey on the blockchain technology, its challenges , advances in managing these challenges and the future of block chain technology in the financial industry.
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.
Gideon Boako, Aviral Kumar Tiwari, David Roubaud
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.
Giray Gözgör, Aviral Kumar Tiwari, Ender Demir, Sagi Akron
No abstract is available for this record.
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.
Kalpanasonika R, Sayasri S M, Vinothini A, Suga Priya H
The accusative of this paper is to predict the bitcoin price accurately by taking various parameters into consideration which affects the bitcoin value. Here multi-layer perceptron algorithms under deep learning are used to predict the price of crypto-currency. Many researchers have analysed the crypto-currency features in many ways such as, market price prediction, the impact of cryptocurrency in real life. It has the ability to make long-term prediction of the exchange price in crypto-currencies particularly in US dollar, based on historical trends. The bitcoin cost prediction is done based on the data set which consists of 13 features relating to the crypto-currency price recorded daily over the period of particular range.
Elie Bouri, Rangan Gupta, Chi Keung Marco Lau, David Roubaud
We study whether level of risk aversion can be used to predict Bitcoin returns. Using a copula-quantile approach, we find evidence of predictability for the lower and upper quantiles of the conditional distribution of returns (i.e., in bull and bear markets). To reveal the sign of the predictability, we apply the cross-quantilogram approach and find that the cross-quantilogram is similar when risk aversion is at the low or medium level for various quantiles of Bitcoin returns. In particular, we find positive predictability when the risk aversion is very low and at the medium level. However, the predictability becomes negative when both the risk aversion and Bitcoin returns are very high, suggesting that very high levels of risk aversion are likely to drive down Bitcoin returns in a bull market.
Gagan Deep Sharma, Mansi Jain, Mandeep Mahendru, Sanchita Bansal · 5 authors
No abstract is available for this record.
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.
Donglian Ma, Hisashi Tanizaki
No abstract is available for this record.
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.
Thomas Fischer, Christopher Krauß, Alexander Deinert
Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al. (2017). With our paper, we pose the question how such a statistical arbitrage approach would fare in the cryptocurrency space on minute-binned data. Specifically, we train a random forest on lagged returns of 40 cryptocurrency coins, with the objective to predict whether a coin outperforms the cross-sectional median of all 40 coins over the subsequent 120 min. We buy the coins with the top-3 predictions and short-sell the coins with the flop-3 predictions, only to reverse the positions after 120 min. During the out-of-sample period of our backtest, ranging from 18 June 2018 to 17 September 2018, and after more than 100,000 trades, we find statistically and economically significant returns of 7.1 bps per day, after transaction costs of 15 bps per half-turn. While this finding poses a challenge to the semi-strong from of market efficiency, we critically discuss it in light of limits to arbitrage, focusing on total volume constraints of the presented intraday-strategy.
Nikolaos Antonakakis, Ioannis Chatziantoniou, David Gabauer
No abstract is available for this record.
Thai Vu Hong Nguyen, Thai Vu Hong Nguyen, Binh Thanh Nguyen, Thanh Cong Nguyen · 6 authors
No abstract is available for this record.
Mike Cudd, Kristen Ritterbush, Marcelo Eduardo, Chris Smith
One of the most significant innovations in the world of finance has been the creation and evolvement of cryptocurrencies. These digital means of exchange have been the focus of extensive news coverage, especially the Bitcoin, with a primary focus on the tremendous potential return and the high level of accompanying risk. In this chapter, we examine the risk-return pattern for an array of cryptocurrencies, contrasting the pattern with those of conventional currency and equity investments. We find the measures of cryptocurrency returns and risk to be a very high multiple of those of conventional investments, and the pattern is determined to be robust relative to the time frame. Consequently, cryptocurrencies are determined to provide an alternative to investors that involves tremendously high risk and return.
Alessandra Cretarola, Gianna Figà‐Talamanca
The goal of this chapter is to present recent developments about Bitcoin1 price modeling and related applications. Precisely, we consider a bivariate model in continuous time to describe the behavior of Bitcoin price and of the investors’ attention on the overall network. The attention index affects Bitcoin price through a suitable dependence on the drift and diffusion coefficients and a possible correlation between the sources of randomness represented by the driving Brownian motions. The model is fitted on historical data of Bitcoin prices, by considering the total trading volume and the Google Search Volume Index as proxies for the attention measure. Moreover, a closed formula is computed for European-style derivatives on Bitcoin. Finally, we discuss two possible extensions of the model. Precisely, we investigate the relation between the correlation parameter and possible bubble effects in the asset price; further, we consider a multivariate framework to represent the special feature of Bitcoin being traded on several exchanges and we discuss conditions to rule out arbitrage opportunities in this setting.