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2,964 results · page 101 of 124
Messaoud Chibane, Nathalie Janson
No abstract is available for this record.
Cyn‐Young Park, Shu Tian, Bo Zhao
No abstract is available for this record.
Daniele Bianchi, Massimo Guidolin, Manuela Pedio
In this paper we take an empirical asset pricing perspective and investigate the dominant view (possibly, an instinctive reflection of the media hype surrounding the surge of Bitcoin valuations) that cryptocurrencies represent a new asset class, spanning risks and payoffs sufficiently different from the traditional ones. Methodologically, we rely on a flexible dynamic econometric model that allows not only time-varying coeficients, but also allow that the entire forecasting model be changing over time. We estimate such model by looking at the time variation in the exposures of major cryptocurrencies to stock market risk factors (namely, the six Fama French factors), to precious metal commodity returns, and to cryptocurrency-specific risk-factors (namely, crypto-momentum, a sentiment index based on Google searches, and supply factors, i.e., electricity and computer power). The main empirical results suggest that cryptocurrencies are not systematically exposed to stock market factors, precious metal commodities or supply factors with the exception of some occasional spikes of the coefficients during our sample. On the contrary, crypto assets are characterized by a time-varying but significant exposure to a sentiment index and to crypto-momentum. Despite the lack of predictability compared to traditional asset classes, cryptocurrencies display considerable diversification power in a portfolio perspective and as such they can lead to a moderate improvement in the realized Sharpe ratios and certainty equivalent returns within the context of a typical portfolio problem.
D. Susana, J. K. Kavisanmathi, S. Sreejith
No abstract is available for this record.
Daniel Felix Ahelegbey, Paolo Giudici, Fatemeh Mojtahedi
No abstract is available for this record.
Cebrail TELEK- Ahmet ŞİT
Cryptocurrency can be defined as a digital asset and a virtual element designed to be an alternative exchange tool for cash in terms of how it works, securing transactions using encryption (cryptography).Looking across the world, there are Bitcoin, Ethereum, Bitcoin Cash, Ripple, Litecoin, Cardano, Nem, Iota, Stellar, Dash and many more cryptocurrencies.The most famous of the cryptocurrencies today is Bitcoin, which is the most preferred in terms of transaction volume and constitutes approximately 50% of the cryptocurrency volume.Bitcoin, created by a person or community named Satoshi Nakatomo in 2008 and the first transfer in 2009, is a digitally created cryptocurrency.The aim of this study is to investigate the relationship between Bitcoin, a cryptocurrency, and gold ounce prices and dollar index.In the study, 2012-2019 was determined as the term and the monthly data were examined.ARDL Boundary test approach was used as a method to determine the cointegration relationship between the examined variables.As a result of the study, a long-term co-integrated relationship between Bitcoin's gold and foreign exchange price was determined.With this result, a 1% increase in the gold ounce price will increase Bitcoin prices by about 15% in the long run; The 1-unit increase in the USD index indicates that it will increase Bitcoin prices by about 0.28%.However, it was concluded that there was no co-integrated relationship between the variables in the short term.
Edwin Ayisi Opare, Kwangjo Kim
Over five thousand digital currencies have been issued by private sector actors since the release of the Bitcoin digital currency in 2009. Private sector issuance of distributed ledger technology (DLT)-based digital currencies such as Bitcoin, Ethereum and other altcoins threaten the stability of financial market infrastructures and preservation of monetary policy. Consequently, many central banks and monetary authorities have begun research and experimentation on central bank-issued digital currencies (CBDCs) to mitigate this threat. In this paper, we present a comprehensive survey of publicly available DLT-based CBDC experiments with completed proof-of-concept prototypes from across the world to enable an understanding of the motivations and best practice approaches for undertaking CBDC experiments. We provide a classification and generic framework for CBDCs and highlight existing DLT platform limitations and use cases in the financial services industry. Overall, our paper organizes in one place, all the relevant, publicly available DLT-based CBDC experiments with completed proof-of-concept prototypes to serve as a reference point for central banks, monetary authorities and researchers desiring to undertake research on DLT-based CBDCs. Ultimately, we present a survey on the technical feasibility and challenges of leveraging DLT to issue the selected CBDC experiments surveyed in this paper.
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.
Chia-Yen Tan, You Beng Koh, Kok Haur Ng, Kooi Huat Ng · 6 authors
No abstract is available for this record.
Maggie Hu, Adrian D. Lee, Tālis J. Putniņš
No abstract is available for this record.
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.
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.
Chamil W. Senarathne, Wei Jianguo
This paper sets out to explore whether the investor herding in the cryptocurrency market induces correlations in cryptocurrency returns using the methodology of Chang et al. (2000) and Galariotis et al. (2015) from a daily data sampling period of 3/30/2015 to 5/24/2019. The initial regression results show that the cross-sectional absolute deviation of return can only be explained by GSCI oil and gold index return, but no relationship exists between cross-sectional absolute deviation of return and other regression variables, such as return on CCi30, US equity risk premium and US/Euro exchange rate return. The herding regression results under normal market condition show that a strong tendency exists to herd on non-fundamental information that explains cross-sectional absolute deviation of returns. As such, cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements). Herding on non-fundamental information is found to be more pronounced during an upward-trending period of the market and other than upward-trending period. No signs of herding on fundamental information could be observed under other market conditions. Although the theory suggests that herding on non-fundamental information results in more efficient outcomes, the above findings do not encourage the diversification of traditional assets with cryptocurrency on the basis of low correlation. Since cryptocurrency lacks intrinsic value, the exchange is shown to provide a pseudo-efficient trading platform for speculative investors. Implications for future research are discussed.
Jinan Liu, Sajjadur Rahman, Apostolos Serletis
Abstract In this paper, we use a bivariate structural VAR to investigate risk spillovers from the cryptocurrency market to standard financial markets. We investigate the effects of cryptocurrency shocks on key financial markets, including the stock, bond, gold and foreign exchange markets. The results show that cryptocurrency shocks do not have statistically significant effects on standard financial markets except for the bond market. This is consistent with most of the existing literature that argues that cryptocurrencies are mostly a new and different asset class, not related to standard factors.
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).
Victoria Dobrynskaya
No abstract is available for this record.
John Taskinsoy
No abstract is available for this record.
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.
Yu Wang, Runyu Chen
With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult. To this end, based on the transaction data of three different markets and the number and content of user comments and responses from online forums, this paper constructs a price prediction model of cryptocurrencies using a variety of machine learning and deep learning algorithms. It turns out that the trading price premium rate in different markets will affect the price to be predicted, and adding social media comment features can significantly improve the accuracy of the forecast. This article is conducive to investors who encrypt currencies to make more scientific decisions.
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.
Olha Holovatiuk
Abstract In this paper, cryptocurrencies are analysed as investment instruments. The study aims to verify whether they can be classified as an asset class and what kind of benefits they may bring to the investor's portfolio. We used 6 indices as proxies for the major asset classes, including the cryptocurrency index CRIX, for all cryptographic assets. Cryptocurrencies relatively fully satisfied 7 asset class requirements, namely stable aggregation, investability, internal homogeneity, external heterogeneity, expected utility, selection skill and cost-effective access. It was found that crypto assets have diversification properties. Portfolio optimisation with the Modern Portfolio Theory showed an increase in the Sharpe ratio of tangency portfolios with the inclusion of CRIX. However, the Post-Modern Portfolio Theory identified significant deterioration of the downside risk and the Sortino ratio.