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3,636 results · page 109 of 152
Emmanouil Christoforou, Ioannis Z. Emiris, Apostolos Florakis
No abstract is available for this record.
Bingbing Dong, Lei Jiang, Jinyu Liu, Yifeng Zhu
We examine how liquidity affects cryptocurrency market efficiency and study commonalities in anomaly performance in cryptocurrency market. Based on the unique features of cryptocurrencies, we build a model with anonymous traders valuing cryptocurrencies as payments for goods and investment assets, and find that decreases in funding liquidity translate into lower asset liquidity in the cryptocurrency market. Empirically, we observe that many widely recognized stock market anomalies also exist in the cryptocurrency market, though some have opposite long/short legs. We also find supportive evidence that a decrease in cryptocurrency liquidity enhances anomaly returns while preventing the cryptocurrency market from achieving efficiency.
Daniel Felix Ahelegbey, Paolo Giudici, Fatemeh Mojtahedi
No abstract is available for this record.
F. N. M. de Sousa Filho, J. N. Silva, Mário Augusto Bertella, Edgardo Brigatti
In this paper, we explore some stylized facts of the Bitcoin market using the BTC-USD exchange rate time series of historical intraday data from 2013 to 2020. Bitcoin presents some very peculiar idiosyncrasies, like the absence of macroeconomic fundamentals or connections with underlying assets or benchmarks, an asymmetry between demand and supply and the presence of inefficiency in the form of strong arbitrage opportunity. Nevertheless, all these elements seem to be marginal in the definition of the structural statistical properties of this virtual financial asset, which result to be analogous to general individual stocks or indices. In contrast, we find some clear differences, compared to fiat money exchange rates time series, in the values of the linear autocorrelation and, more surprisingly, in the presence of the leverage effect. We also explore the dynamics of correlations, monitoring the shifts in the evolution of the Bitcoin market. This analysis is able to distinguish between two different regimes: a stochastic process with weaker memory signatures and closer to Gaussianity between the Mt. Gox incident and the late 2015, and a dynamics with relevant correlations and strong deviations from Gaussianity before and after this interval.
Johannes Jakubik, Abdolreza Nazemi, Andreas Geyer-Schulz, Frank J. Fabozzi
In this paper, we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose that includes financial news significantly outperforms machine learning models without financial news. In the first stage, we leverage long short-term memory (LSTM) networks to extract structured information from financial news. In the second stage, we apply machine learning models with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to machine learning models without input from financial news, we find that the out-of-time rate of return attained with the proposed forecasting system is substantially higher than for a buy-and-hold strategy. Our study highlights how combining deep learning and financial news offers investors and traders support for the monetization of unstructured data in finance.
Dilek Teker, Suat Teker, Mustafa Özyeşil
Cryptocurrency is a recent and popular topic that attracts the interest of investors and fund managers. Beyond the market discipline, researchers question the interaction between cryptocurrencies and macroeconomic variables. This study focuses on how the changes in gold and oil prices affect the daily price movements of various cryptocurrencies. The daily database used in this study includes the prices of the cryptocurrencies such as Bitcoin, Tether, Ethereum, Litecon and EOS for the period of August 1, 2017 and April 3, 2019. Initially, the stationarity of the time series is tested by The existence of the cointegration relationship among the series is tested by The presence of causality relationships among the series is investigated with the Dolado and Ltkepohl (1996) causality test. The empirical results support that there exists a cointegration relationship only in between Tether and gold and oil prices.
Hanna Danylchuk, Oksana Kovtun, Liubov Kibalnyk, Oleksii Sysoiev
The paper focuses on monitoring and modelling of the cryptocurrency market. The application of the chosen research methods is based on the analysis of existing methods and tools of economic and mathematical modelling of time series research on the example of the cryptocurrency market. It is proved that the use of individual methods is not relevant, as they do not give an adequate assessment of the specified market, so a comprehensive approach is the most acceptable. Therefore, monitoring and modelling of some cryptocurrency pairs with different capitalization degree were implemented by fractal and recurrent methods of the financial markets. The daily values of currency pairs for the period from September 2015 to November 2019 were chosen as information basis for monitoring and modelling. The use of R /S modelling method make it possible to conclude the persistence of time series of the selected cryptocurrencies indicating that the market trends are clearly defined, the currency pair of XRP/USD has the highest level of trend resistance. To compare the obtained results, the comprehensive approach is offered using recurrent diagrams that help to determine the cryptocurrency stability. The results of modelling by the recurrent method show that the most stable cryptocurrencies are the ones with the highest capitalization, namely Bitcoin and Ripple.
Daniel Traian Pele, Niels Wesselhöfft, Wolfgang Karl Härdle, Michalis Kolossiatis · 5 authors
No abstract is available for this record.
Alexander Thoma
This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of cryptocurrencies from 2014 to 2020. I find a strong outperformance over the market, even after controlling for known predictors. Factor regressions with a cryptocurrency three-factor model further reveal significant alphas. Robustness test emphasize the legitimacy of the strategy. On average, cryptocurrencies with a high (low) prospect theory value earn low (high) subsequent returns. Interestingly, traders in the cryptocurrency market seem to assess the attractiveness of cryptocurrency in a way described by prospect theory. Mechanical tests of the model show that probability weighting is a main driver behind this assessment. Cryptocurrencies with a high prospect theory value tend to be highly positively skewed. This skewness could be the reason why the cryptocurrency seems attractive to traders, similar to lottery-like gambles.
Daniele Bianchi, Luca Rossini, Matteo Iacopini
We study the cross-sectional interdependence between returns on cryptocurrency pairs and deviations of Tether USD from its parity to the U.S. dollar. Methodologically, we propose a large-scale Bayesian Vector Autoregressive (BVAR) model which features a global-local shrinkage prior for cross-pairs return correlations. Empirically, we show that deviations from the USDT/USD parity significantly and positively correlate with future returns on cryptocurrency pairs, conditional on both aggregate and asset-specific trading activity. A simple long-only rotational investment strategy which exploits the exposure to the lagged USDT/USD deviations outperforms out-of-sample passive benchmark investments in Bitcoin and a value-weighted market index.
Nam-Kyoung Lee, Eojin Yi, Kwangwon Ahn
No abstract is available for this record.
Mohd Sabri Ismail, Saiful Izzuan Hussain, Mohd Salmi Md Noorani
This study explores persistent homology to detect early warning signals of the 2017 and 2019 major financial crashes in Bitcoin. Sliding window is used to obtain point cloud datasets from a multidimensional time series (Bitcoin, Ethereum, Litecoin and Ripple). We apply persistent homology to quantify transient loops that appear in multiscale topological spaces, which associated on each point cloud dataset and encode the quantified information in a persistence landscape. Temporal changes in persistence landscapes are measured via their L1-norms. Consequently, a new representative is attained, called L1-norms time series. The L1-norms is associated with indicators: autocorrelation function at lag 1, variance and mean power spectrum at low frequencies to detect the signals. By using Kendall's tau correlation and significance test, significant rising trend events that occur before major financial crashes in Bitcoin are defined as the signals. A threshold is determined to scan entire data and record all the significant rising trend events. Lastly, we compare L1-norms with residuals time series, which is another representative obtained from de-trending approach. Our result portrays that autocorrelation function at lag 1 and variance of the L1-norms successfully detect early warning signals before the 2017 and 2019 major financial crashes. However, variance of the L1-norms is better since it able to signal another 2018 major financial crash. For the residuals, no early warning signals are detected. Hence, persistent homology provides a better representative than de-trending approach. Overall, persistent homology is a promising method to detect early warning signals of major financial crashes in Bitcoin.
Lennart Ante, Ingo Fiedler
No abstract is available for this record.
Thomas Dimpfl, Dalia Elshiaty
Purpose Cryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the cryptocurrency markets contributes the most to the common volatility component inherent in the market. Design/methodology/approach The paper extracts each of the cryptocurrency's markets' latent volatility using a stochastic volatility model and, subsequently, models their dynamics in a fractionally cointegrated vector autoregressive model. The authors use the refinement of Lien and Shrestha (2009, J. Futures Mark) to come up with unique Hasbrouck (1995, J. Finance) information shares. Findings The authors’ findings indicate that Bitfinex is the leading market for Bitcoin and Ripple, while Bitstamp dominates for Ethereum and Litecoin. Based on the dominant market for each cryptocurrency, the authors find that the volatility of Bitcoin explains most of the volatility among the different cryptocurrencies. Research limitations/implications The authors’ findings are limited by the availability of the cryptocurrency data. Apart from Bitcoin, the data series for the other cryptocurrencies are not long enough to ensure the precision of the authors’ estimates. Originality/value To date, only price discovery in cryptocurrencies has been studied and identified. This paper extends the current literature into the realm of volatility discovery. In addition, the authors propose a discrete version for the evolution of a markets fundamental volatility, extending the work of Dias et al. (2018).
Minjung Park, Sangmi Chai
With the increase in the attention to cryptocurrency, studies on the factors affecting the price fluctuation of cryptocurrency have been actively conducted. Prior researches suggested that policy announcements (i.e., public information) related to cryptocurrency have been found to affect the price volatility in the market in particular. Privileged information, which is hard to be observable unlike public information published by the government or corporations, is hardly homogenously distributed to individual investors. However, it inevitably affects the price in any market. Therefore, this study aims to identify the information asymmetry, which is mainly formed by privileged information, in the cryptocurrency market. Moreover, this study examines whether investment sentiment, which mainly influences transaction behaviors of uninformed traders, has a significant effect on the cryptocurrency market as well. The results contribute to the understanding of the cryptocurrency market in a basis of the existing market theories.
Victoria Dobrynskaya
No abstract is available for this record.
Benjamin M. Blau, Todd G. Griffith, Ryan J. Whitby
This study examines the comovement between 17 of the most active cryptocurrencies. We are unable to statistically reject the presence of perfect comovement between Bitcoin and six of the 16 non-Bitcoin cryptocurrencies. Consistent with the friction-based explanation for the presence of comovement, once the CBOE introduced futures contracts on Bitcoin, we find that all 16 cryptocurrencies comove with Bitcoin. These results suggest that introducing futures contracts improves the informational environment of the entire cryptocurrency market, which helps explain the unusual comovement in the cryptocurrency market.
Jun Deng, Huifeng Pan, Shuyu Zhang, Bin Zou
We formulate an optimal hedging problem of Bitcoin inverse futures under the minimum-variance framework. We obtain the optimal hedging strategy in closed forms for both short and long hedges and compute hedging effectiveness under the optimal strategy. Our empirical analyses show that the optimal hedging strategy achieves superior effectiveness in reducing risk and outperforms the naïve hedge in all scenarios.
Yulin Liu, Luyao Zhang
Currently, there are no convincing proxies for the fundamentals of cryptocurrency assets. We propose a new market-to-fundamental ratio, the price-to-utility (PU) ratio, utilizing unique blockchain accounting methods. We then proxy various existing fundamental-to-market ratios by Bitcoin historical data and find they have little predictive power for short-term bitcoin returns. However, PU ratio effectively predicts long-term bitcoin returns than alternative methods. Furthermore, we verify the explainability of PU ratio using machine learning. Finally, we present an automated trading strategy advised by the PU ratio that outperforms the conventional buy-and-hold and market-timing strategies. Our research contributes to explainable AI in finance from three facets: First, our market-to-fundamental ratio is based on classic monetary theory and the unique UTXO model of Bitcoin accounting rather than ad hoc; Second, the empirical evidence testifies the buy-low and sell-high implications of the ratio; Finally, we distribute the trading algorithms as open-source software via Python Package Index for future research, which is exceptional in finance research.
Larisa Yarovaya, Damian Zięba
This paper analyses the volume-return relationships across the top 30 most traded cryptocurrencies from April 2013 to June 2019 using high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founder’s origin, platform on which the cryptocurrency is built, and consensus algorithm, among others. We identify significant bidirectional causalities between trading volume and returns at different high-frequency intervals; however, those linkages are weakening with decreasing data frequencies. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency markets providing useful insights from cryptocurrency categorisation.
Georg Keilbar, Yanfen Zhang
Abstract This paper aims to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying systemwide cointegration exposure. Our results show that cryptocurrencies are indeed cointegrated with a cointegration rank of four. We also find that all currencies are affected by these long term equilibrium relations. The nonlinearity in the error adjustment turned out to be stronger during the height of the cryptocurrency bubble. A simple statistical arbitrage trading strategy is proposed showing a great in-sample performance, whereas an out-of-sample analysis gives reason to treat the strategy with caution.
Timothy Peterson
No abstract is available for this record.
Sangjin Park, Kwahngsoo Jang, Jae‐Suk Yang
No abstract is available for this record.
Ambreen Khursheed, Muhammad Abubakr Naeem, Sheraz Ahmed, Faisal Mustafa
This study examines the adaptive market hypothesis (AMH) in relation to time-varying market efficiency by using three tests, namely Generalized Spectral (GS), Dominguez-Lobato (DL) and the automatic portmanteau test (AP) test on four-digital currencies; Bitcoin, Monaro, Litecoin, and Steller over the sample period of 2014–2018. The study applies Jarque-Bera test, ADF test, Ljung-Box statistics and ARCH-LM test for testing normality of returns, stationarity of series, serial correlation and volatility clustering in returns and squared returns of selected cryptocurrencies. Further, the study adopts an extremely important category of martingale difference hypothesis (MDH), which uses non-linear methods of dependencies for identifying changing linear and non-linear dependence in the price movement of currencies. The results indicate that price movements with linear and nonlinear dependences varies over time. Our tests also reveal that Bitcoin, Monaro and Litecoin have the longest efficiency periods. While Steller shows the longest inefficient market period. In view of varying market conditions, the results indicate that different market periods have significant impact on prices fluctuations of cryptocurrencies. Therefore, our findings suggest implementing the adaptive market hypothesis (AMH) as predicting changes in cryptocurrency prices over time must consider the time-varying market conditions for efficient forecasting.