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4,843 results · page 176 of 202
Ashish Rajendra Sai, Jim Buckley, Andrew Le Gear
No abstract is available for this record.
Z. Keskin, Tomaso Aste
Information transfer between time series is calculated using the asymmetric information-theoretic measure known as transfer entropy. Geweke’s autoregressive formulation of Granger causality is used to compute linear transfer entropy, and Schreiber’s general, non-parametric, information-theoretic formulation is used to quantify nonlinear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect statistical causality between social sentiment changes and cryptocurrency returns. We validate results by performing permutation tests by shuffling the time series, and calculate the Z -score. We also investigate different approaches for partitioning in non-parametric density estimation which can improve the significance. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, with greater net information transfer from sentiment to price for XRP and LTC, and instead from price to sentiment for BTC and ETH. We report the scale of nonlinear statistical causality to be an order of magnitude larger than the linear case.
Zheng Nan, Taisei Kaizoji
No abstract is available for this record.
OlaOluwa S. Yaya, Ahamuefula E. Ogbonna, Olusanya E. Olubusoye
No abstract is available for this record.
Oscar Delgado-Mohatar, Marta Felis-Rota, Carlos Fernández-Herráiz
No abstract is available for this record.
Wonse Kim, Junseok Lee, Kyungwon Kang
This paper investigates the effects of the launch of Bitcoin futures on the intraday volatility of Bitcoin. Based on one-minute price data collected from four cryptocurrency exchanges, we first examine the change in realized volatility after the introduction of Bitcoin futures to investigate their aggregate effects on the intraday volatility of Bitcoin. We then analyze the effects in more detail utilizing the discrete Fourier transform. We show that although the Bitcoin market became more volatile immediately after the introduction of Bitcoin futures, over time it has become more stable than it was before the introduction.
Chongyang Bai, Tommy E. White, Linda Xiao, V. S. Subrahmanian · 5 authors
We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time --- rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Student's t-test with p<1e-5. Check our demo at https://www.cs.dartmouth.edu/dsail/demos/c2p2
Ladislav Krištoufek
No abstract is available for this record.
Ladislav Krištoufek, Miloslav Vošvrda
No abstract is available for this record.
Chamil W. Senarathne
Abstract This paper examines the impact of Libra on volatility of Bitcoin using the classical framework of C. G. Lamoureux and W. D. Lastrapes (1990). ARCH and GARCH effects disappear when lagged ICO funding size is included in the variance equation. A negative association between volatility and funding size and the disappearance of volatility persistence (long-term volatility effect) suggest that Libra, as a dominant new currency, is likely to stabilize the cryptocurrency market and enhance potential for currency diversification. Furthermore, it is revealed that the stability cannot be ensured merely by backing decentralized blockchain instruments, such as Bitcoin, with bank deposits, government securities or exchange rate.
Elie Bouri, Κωνσταντίνος Γκίλλας, Rangan Gupta
Abstract In this paper, we first estimate the monthly realised correlation, based on daily data, between stock returns of the United States (US) and Bitcoin returns. Then, we relate the realised correlation over the period October 2011 to May 2019 with a news‐based measure of the growth of trade uncertainty of the US. Our results show that the realised correlation is negatively impacted by increases in trade uncertainty, which continues to hold under alternative robustness checks, suggesting that Bitcoin can act as a hedge relative to the conventional stock market in the wake of heightened trade policy‐related uncertainties, and provide diversification benefits for investors.
Qiang Ji, Elie Bouri, David Roubaud, Ladislav Krištoufek
No abstract is available for this record.
Camilla Muglia, Luca Santabarbara, Stefano Grassi
The paper investigates whether Bitcoin is a good predictor of the Standard & Poor’s 500 Index. To answer this question we compare alternative models using a point and density forecast relying on Dynamic Model Averaging (DMA) and Dynamic Model Selection (DMS). According to our results, Bitcoin does not show any direct impact on the predictability of Standard & Poor’s 500 for the considered sample.
İbrahim Korkmaz Kahraman, Habib Küçükşahin, Emin ÇAĞLAK
Forecasting models based on the assumption that returns are normally distributed do not perform sufficiently on shallow markets. These models are more likely to fail in the estimation of the extreme points that can be reached especially at high volatility markets, and this situation is led to investors in predicting volatility. In the volatility forecasting of crypto money, which is seen as an alternative investment tool for the financial investors, single volatility models such as, ARCH, GARCH, T-GARCH, GARCH-M, E-GARCH, and I-GARCH and long memory models (AP-GARCH and C-GARCH) was utilized. In addition, the most suitable model was tried to be tested among the models used for volatility estimation. In this context, the price data of Bitcoin, Ethereum and Ripple cryptocurrency with the highest market value in the crypto money market have been utilized between 24/08/2016-07/05/2018. According to the results of the research, for Bitcoin and Ethereum, the volatility effect of the shocks is permanent and the effect of the positive shocks is more than that of the negative shocks, whereas for Ripple, the volatility effect of the shocks is transient and the passivity of the volatility is short.
Muhammad Owais Qarni, Saqib Gulzar, Syeda Tamkeen Fatima, Majid Jamal Khan · 5 authors
This paper investigates the volatility spillover dynamics between U.S. Bitcoin and financial markets from July 19, 2010 to December 29, 2017. Diebold and Yilmaz (2012) volatility spillover index, Barunik, Kocenda, and Vacha (2017) Spillover Asymmetry Measure, and Barunik and Krehlik (2018) frequency connectedness methodologies are applied to investigate the time varying dynamics of volatility spillover among U.S. Bitcoin and financial markets. The findings of the study indicate the presence of low level of integration and contagion between U.S. Bitcoin and financial markets. Asymmetric nature of volatility spillover is also detected. The connectedness among the U.S. Bitcoin and financial markets is found to be concentrated at high frequency, suggesting that markets process information rapidly. Moreover, the turbulence in Bitcoin market will have insignificant effect on U.S. financial markets. This non-contagion nature of Bitcoin markets provides significant risk hedging and diversification benefits for domestic and foreign investors in the U.S.
Min Shu, Wei Zhu
In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifying bubbles using the daily data on Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and finer (than daily) timescale for the Bitcoin price data. We adopt two levels of time series, 1 hour and 30 minutes, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale - on a day to week scale, and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale - on a week to month scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast of crashes to warn of the imminent risk.
Christian Masiak, Joern Block, Tobias Masiak, Matthias Neuenkirch · 5 authors
Abstract We apply a vector autoregression (VAR) model to investigate the market cycles of Initial Coin Offerings (ICOs) as well as their relationships with bitcoin and ether. Our sample covers 104 weekly observations between January 2017 and December 2018. Our results show that ICO market cycles exist and that shocks to the growth rates of ICO volumes are persistent. In addition, shocks in cryptocurrency returns have a substantial and positive effect on ICO volumes. In contrast, the volatility of cryptocurrency returns does not significantly affect ICO volumes. Our results are robust to using (i) the number of successfully completed ICO campaigns instead of ICO volumes and (ii) ICO data from a different data source. Our study has implications for financial practice, in particular for cryptocurrency investors and entrepreneurial firms conducting ICOs.
Vasileios Kallinterakis, Ying Wang
No abstract is available for this record.
Łukasz Goczek, Ivan Skliarov
This article aims to determine what drives the price of Bitcoin. To achieve this aim, a large set of data is analysed using VEC models augmented by factors representing unobservable economic forces. They have been obtained by means of principal component analysis. This method enables us to contribute to the existing literature on Bitcoin in two ways. First, we employ the dimension reduction technique to combine variables from several papers. Second, we estimate several unobservable economic concepts instead of utilizing proxy variables as is usually done. We find that the main factor driving the Bitcoin price is its popularity. Hence, our result not only confirms some previous findings but reinforces them by providing a better definition of popularity. Finally, we conclude that the Bitcoin price is not affected by supply and demand factors in the way that is natural for conventional currencies.
Reaz A. Chowdhury, M. Arifur Rahman, M. Sohel Rahman, M. R. C. Mahdy
At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.
Andrew Clark, Alexander Mihailov
This paper begins by a recap on the ambition and mechanism behind Bitcoin, followed by an overview of the top 10 cryptocurrencies by market capitalization. Our focus is on their price dynamics and volatility relative to those of fiat paper money and gold, assets that have traditionally served the functions of money and international reserves. We then perform a counterfactual analysis using the Bank of England's foreign currency reserves to determine the hypothetical performance in terms of relative volatility of two alternative reserve portfolios consisting of 0.1%, 1%, or 10% holdings of either Bitcoin only, since July 2010, or of a portfolio of 50% Bitcoin and 50% Ethereum, since July 2015. Revisiting in this light the functions of money and international reserves, we expound on why private cryptocurrencies do not meet the inherent requirements for both money and international reserve assets, whereas central bank digital currencies do meet these requirements. We, finally, "scale" the magnitude and dynamics of the recent Bitcoin bubble into a historical perspective, and conclude by a discussion of areas where blockchain-based and FinTech technologies could be beneficial in international trade, payments, banking and finance.
Lanouar Charfeddine, Noureddine Benlagha, Youcef Maouchi
No abstract is available for this record.
Jamal Bouoiyour, Refk Selmi
Bitcoin futures were launched by the Chicago Board of Options Exchange and the Chicago Mercantile Exchange group on December 18th, 2017. This study stands as a first attempt to explore the reactions of Bitcoin spot market to the launch of futures contracts. Using an event-study methodology and an adjusted asset pricing model, we show that Futures trading drove up the price of Bitcoin immediately after the announcement day. This reaction started to decrease noticeably following the launch of the futures contracts. Such outcome seems in line with the trading behavior that typically accompanies the launch of futures markets for an asset.
Vítor Manuel Araújo da Fonseca, Manuel A. R. da Fonseca
This article’s goal is to evaluate if the recent price behavior of Bitcoin can be characterized as a financial market “bubble”. To deal with this assessment, we adopt a statistical definition of a “bubble” derived from the efficient market hypothesis and we propose a simple method to test this proposition, based on the time-series model known as random walk. We analyze the data available for Bitcoin prices, together with an asset selected as benchmark, and perform statistical tests derived from simple regression equations. The main conclusion is that there is consistent evidence that that Bitcoin follows the pattern of a financial “bubble” – at least, such pattern is more evident in the case of Bitcoin than in the stock index used as benchmark.