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4,843 results · page 141 of 202
Aktham Maghyereh, Hussein Abdoh
No abstract is available for this record.
Rong Li, Sufang Li, Di Yuan, Huiming Zhu
No abstract is available for this record.
Rubaiyat Ahsan Bhuiyan, Afzol Husain, Ch. Zhang
No abstract is available for this record.
Emily Fletcher, Charles Larkin, Shaen Corbet
No abstract is available for this record.
Demet Eroğlu Sevinç, Gönül Yüce Akıncı
The development process in financial markets give rise to the emergence of various financial instruments and cryptocurrencies, which are the newest tools of this process, are trying to integrate into the system. Even though the use of crypto-currencies for investment and speculation has increased, limited information on the market leads to high level of volatility in price and return. Therefore, this study aims to analyze the volatility dynamics of the returns of Bitcoin, which is the cryptocurrency with the largest market volume, using the weekly data set for 2013:04-2020:09 period. In this context, Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model is employed to investigate the asymmetric volatility, which refers to the asymmetric effects of positive and negative shocks. The results of the analysis show that the leverage effect applies to Bitcoin returns. In other words, the asymmetric effect between good and bad news is revealed. Moreover, the fact that the parameter of the volatility resistance has a high value reflects that the asymmetric past period shocks have a significant effect on the current period conditional variance.
Dimitrios Anastasiou, Antonis Ballis, Κωνσταντίνος Δράκος
No abstract is available for this record.
Jingjing Wang, Xiaoyang Wang
No abstract is available for this record.
Luca Fantacci, Lucio Gobbi
Abstract Stablecoins are second generation cryptocurrencies, aimed at maintaining their value stable with respect to official currencies. The most famous example is perhaps represented by libra, the cryptocurrency announced by Facebook in 2019 and yet to be issued; the most widespread is tether, with a market capitalization of almost 10 billion dollars and a daily transaction volume of almost 50 billion dollars, which makes it the most used cryptocurrency. The diffusion of stablecoins is hardly surprising. By minimizing volatility – the main flaw of first generation cryptocurrencies, including bitcoin –, stablecoins are expected to play an even more important role on a global scale within a few years. Our contribution deals not with the economic, but specifically with the geopolitical factors that could foster the use of stablecoins for strategic and military purposes. In particular, we focus on how such payment instruments, together with other alternative electronic payment systems, could be used as a means to circumvent economic sanctions and ultimately as a challenge to the hegemony of the US dollar in the international monetary system.
Jihed Majdoub, Salim Ben Sassi, Azza Béjaoui
No abstract is available for this record.
Syed Jawad Hussain Shahzad, Elie Bouri, Sang Hoon Kang, Tareq Saeed
The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.
Hélder Sebastião, Pedro Godinho
This study examines the predictability of three major cryptocurrencies-bitcoin, ethereum, and litecoin-and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.
Akanksha Jalan, Roman Matkovskyy, Andrew Urquhart
Bitcoin futures were introduced in December 2017 and this was seen by some as a sign of the most popular cryptocurrency finally being accepted by the financial community. In this paper, we examine the impact of the introduction of Bitcoin futures on the Bitcoin spot market in terms of five characteristics – returns, volatility, skewness, kurtosis and liquidity, using a Bayesian diffusion-regression (state-space) structural time-series model. Our results indicate that the introduction of bitcoin futures potentially exerted a downward impact on the USD bitcoin spot market return and skewness and an upward one on volatility, kurtosis and liquidity, which became higher after futures were introduced. Therefore, our paper offers important insights for investors and regulators, while providing some guidance as to the potential impact of futures markets on other cryptocurrencies.
Dirk G. Baur, Thomas Dimpfl
No abstract is available for this record.
Abdulnasser Hatemi‐J, Mohamed Ali Hajji, Elie Bouri, Rangan Gupta
This paper investigates the potential portfolio diversification between Bitcoin, bonds, equities, and the US dollar. We make use of two approaches for constructing the portfolio. The first is the standard minimum variance approach, and the alternative is based on combining risk and return when the portfolio is constructed. The portfolio based on the minimum variance approach does not result in increasing the return per unit of risk compared to the corresponding value for the best single asset, in this case, Bitcoin. However, the portfolio based on the approach that combines risk and return in the optimization problem does show a return per unit risk higher than the corresponding value for any of the four assets. Thus, the portfolio diversification benefit with respect to these four assets, in terms of return per unit risk, exists only if the portfolio is constructed via the new approach.
Carol Alexander, Jun Deng, Bin Zou
We consider the hedging problem where a futures position can be automatically\nliquidated by the exchange without notice. We derive a semi-closed form for an\noptimal hedging strategy with dual objectives - to minimise both the variance\nof the hedged portfolio and the probability of liquidations due to insufficient\ncollateral. The optimal solution depends on the statistical characteristics of\nthe spot and futures extreme returns and parameters that characterise the\nhedger by loss aversion, choice of leverage and collateral management. An\nempirical analysis of bitcoin shows that the optimal strategy combines superior\nhedge effectiveness with a reduction in the probability of liquidation. We\ncompare the performance of seven major direct and inverse hedging instruments\ntraded on five different exchanges, based on minute-level data. We also link\nthis performance to novel speculative trading metrics, which differ markedly\nbetween venues.\n
Yan Li, Zhicheng Wang, Hongchuan Wang, Meiyu Wu · 5 authors
No abstract is available for this record.
Imran Yousaf, Shoaib Ali
This study explores the return and volatility spillovers between S&P 500 and cryptocurrencies [Litecoin (LTC), Bitcoin (BTC) and Ethereum (ETH)] during the pre-COVID-19 period and COVID-19 period using the VAR–BEKK–AGARCH model on hourly data. Furthermore, this study also quantifies the optimal portfolio weights and hedge ratios during both sample periods. The findings of study show that the return and volatility spillovers between the US stock and cryptocurrency markets are not significant during the pre-COVID-19 period. However, the study finds unidirectional return transmission from S&P 500 to all the cryptocurrencies during the COVID-19 period. During the COVID-19 period, the volatility spillover is unidirectional from S&P 500 to Litecoin, whereas the volatility transmissions are not significant for the pairs of S&P 500–Bitcoin and S&P 500–Ethereum. Based on optimal weights, the portfolio managers are recommended to slightly decrease their investments in S&P 500 for the portfolios of S&P 500/BTC, S&P 500/ETH and S&P 500/LTC during the COVID-19 period. Finally, during the COVID-19 period, all hedge ratios were found to be higher, implying higher hedging costs during the COVID-19 period compared to the pre-COVID-19 period. Our research offers valuable insights to the fund managers, investors and policymakers regarding diversification opportunities, hedging, optimal asset allocation and risk management.
Nick James
This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories, extremes, and show greater persistence in anomalies over time.
Ioannis E. Livieris, Stavros Stavroyiannis, Emmanuel Pintelas, Theodore Kotsilieris · 5 authors
No abstract is available for this record.
Achraf Ghorbel, Ahmed Jeribi
No abstract is available for this record.
Zhenghui Li, Liming Chen, Hao Dong
No abstract is available for this record.
Ali Yazbek
No abstract is available for this record.
Deni Memić, Selma Skaljic Memic, Mohamed Noor Saifuddin Mohamed Noor Saif Almehairi
We observe the relationship and causality between cryptocurrencies on one, and commodities, currencies, equity indexes and web search results on the other side. We use prices of Bitcoin and Ethereum for cryptocurrencies, prices of crude oil and gold for commodities, Euro-US Dollar, Euro-Swiss Franc exchange rates for currencies, Dow Jones Industrial Average for market index and Google Trends® data as a measure of worldwide web search results for cryptocurrencies of interest. We find that Bitcoin and web search results correlation went from highly positive to low negative during the COVID-19 period. The results of the study show that the price of Bitcoin and Ethereum can be modelled using different combinations of commodities, currencies, indexes and web search results, with web search results and Dow Jones Industrial Average exhibiting best predictive power both concurrently and one day in advance. Our best performing models were able to explain more than 95% and 90% of Bitcoin and Ethereum price variability respectively. We also find strong evidence of web search traffic impacting both Bitcoin and Ethereum prices at all tested lags, as well as some evidence of gold impact on Bitcoin and EUR/CHF impact on Ethereum.
S. Santhosh Kumar
Bitcoin and other cryptocurrencies are subject to unusual price fluctuations that increase the concern of the people and institutions to transact with it and to invest in it. The daily price volatility in the case of Bitcoin scales even up to 50 per cent in some days. Studies on market efficiency, volatility, demand drivers and so on of Bitcoin are done on considerable scale to bring out pertinent information about its different behavioural dimensions. However, its acceptance and use are limited primarily on account of the volatility and the political risks associated to it. This paper pioneers in the assessment of the temporal sequence and magnitude of volatility of Bitcoin by analysing 2018 daily price data. The study found that the coin shows unusually high daily price changes of 10 per cent or more only on 70 days (3.47%) out of the 2017 daily returns computed from the price data. Noticeably, the number of days with positive returns in the 70 days is 31 as against the 39 days with losses. These unusual daily price changes of 3 to 4 times out of 100 found in the study are the cause of volatility concern spreading around Bitcoin. There are six instances of more than 100 days gap between the two unusual price changes in the 70 cases. No significant correlation is found between the unusual daily returns and the corresponding volume of trade on these days. The unusual daily volatility of Bitcoin occurring once in a while may dampen its role as a store of value and a unit of account.