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2,964 results · page 85 of 124
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.
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
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.
Achraf Ghorbel, Ahmed Jeribi
No abstract is available for this record.
Ali Yazbek
No abstract is available for this record.
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.
Kei Nakagawa, Ryuta Sakemoto
Both cryptocurrencies and gold are scarce, expensive for extraction, and less affected by money supply. We focus on these similarities and investigate whether cryptocurrency network affects impact on expected return on gold. Our results show that the number of cryptocurrency wallet users is positively related to the expected return on gold. Moreover, we employed a machine-learning approach and considered the interactions among predictors. We reveal that network factors have a greater impact on gold than returns on Bitcoin and other macroeconomic and financial variables.
Zhang Mengshi, Daniel L. Jia
The blockchain technology and cryptocurrency are now in the centre of the financial market. The raise of the cryptocurrencies represented by Bitcoin have attracted a large group of scholars to analyze the underlying dynamics of their price fluctuations. Intensive debate emerged on the intrinsic features of Bitcoin. In theoretical analysis, we developed the principle of monetary convention to define the concept of monetary consensus, capturing the nature of monetary system, and categorize it into three types: traditional, algorithm and hybrid. Based on the Wavelet Coherence Analysis, we try to analyze Bitcoin price dynamics in both time and frequency domains, comparing Bitcoin with financial assets, economic and financial indexes, and other cryptocurrencies.
Nataliia Kuzminska, М. О. Фалько, Nikita Zaharov
The article analyzes the impact of 44 different factors on the change in the price of bitcoin, such as: yields on US one-year bonds, prices for futures on palladium, platinum, gold, silver, copper, Chinese stock indices, German stock index DAX, French index CAC 40, stock indices of South Africa, India, Mauritius, England, Switzerland, Sweden, South Korea, etc. The most influential factors were selected, with the help of which two regression models were built. The presence of insignificant factors in the first model led to the need to consider the second one, which is much better. The second model lacks multicollinearity, but there are autocorrelation of residues and heteroscedasticity. There was an attempt to eliminate autocorrelation of residues, but it led to a significant reduction in the coefficient of determination to 22%. An attempt to explain the causality of the Bitcoin exchange rate to the KOSPRI index has been made. South Korea is a country, which actively cooperates with two main leaders in Bitcoin mining, namely China, where 65 % of new bitcoins are created, and the United States – 23 % of new bitcoins. Modern economic leaders actively influence not only the economy of their country, but also other economies. Korean companies are actively penetrating both the Chinese economy and the US national economy, which determines their impact on Bitcoin mining, as a large amount of necessary electronics is produced by South Korean companies such as SK hynix Inc. As for the palladium futures rate, palladium is a metal which is actively used in production of the electronics needed to mine bitcoin. Prospects for further research depend on trends in the price of bitcoin. It is possible that the price of the cryptocurrency Bitcoin will gradually rise, as some corporations begin to recognize Bitcoin as a means of payment for goods and services. This will increase demand and, as a result, prices. The confidence of large companies in Bitcoin will indicate the reliability of the cryptocurrency, which in turn will give confidence to minority investors in the need to add cryptocurrency to investment portfolios. The rise in prices will indicate the need to study the impact of various factors on changes in the price of Bitcoin. If the popularity of cryptocurrency begins to wane, it will lead to a rapid loss of its price. This development option will indicate the inexpediency of continuing to study the relationship of this cryptocurrency with these factors and the need to find other ones.
Chen Jin, Bowen Lou, Jiding Zhang
No abstract is available for this record.
Bryan Williams
No abstract is available for this record.
S Padmavarthini
No abstract is available for this record.
Agustin Alba Chicar, Pablo Roccatagliata, Matías Zabaljauregui
No abstract is available for this record.
Ishfaque Ahmed Soomro, Suresh Kumar Oad Rajput, Nadia Anjum, Najma Ali Soomro
No abstract is available for this record.
Natividad Blasco, Pilar Corredor
No abstract is available for this record.
Moazzam Khoja
No abstract is available for this record.
Didik Gunawan, Mangasi Sinurat, Lukito Cahyadi, Rico Nur Ilham
This study aims to examine the dynamic relationship between the JKSE, S&P 500, gold prices, and bitcoin prices after WHO declared Covid-19 a global pandemic. The data used is daily data from March to November 2020 which follows trading days in the Indonesian capital market. Furthermore, this research uses VAR modelling to see how the impact of the Covid-19 pandemic on the relationship between the JCI, S&P 500, gold prices and bitcoin prices. The results showed that in the short term the S&P 500 has a positive and significant effect on JKSE, but in the long run it has no significant positive effect, in the long run the gold price has a negative and significant effect on JKSE and vice versa has no effect in the short term, both in the long term and in the short-term bitcoin has a negative and significant effect on JKSE. This research also shows that apart from gold, bitcoin has also become a safe haven for investors.
Adedeji Daniel Gbadebo, Ahmed Oluwatobi Adekunle, Wole Adedokun, Adebayo-Oke Abdulrauf Lukman · 5 authors
This paper offers a plausible response to “what explains the sporadic volatility in the price of Bitcoin?” We hypothesized that market “fundamentals” and “information demands” are key drivers of Bitcoin’s unpredictable price fluctuation. We adopt the transfer-function [Autoregressive Distributed Lag, ARDL] model and its Bounds testing approach to verify how the volatility of the price of Bitcoin responds to its transaction volume, cryptocurrency market capitalisation, world market equity index and Google search. We found the existence of long-run cointegration relation and observed that all the variables except the equity index positively explain the volatility of Bitcoin price. The result established evidence that market fundamentals drive erratic swing in Bitcoin price than information.
Triasesiarta Nur, Narendra Dewangkara
Cryptocurrency works on a system that admits people to make payments all over the world without the requirement for any intermediary. Most digital currencies experience frequent periods of intense volatility. This paper examines the day of the week effects in return and volatility on Bitcoin, Ethereum, Ripple, Litecoin, and Tether currencies. To estimate volatile variance, this research uses five ARCH family models: ARCH, GARCH, EGARCH, TARCH and PARCH Models. The best models are derived based on Akaike Info Criterion and Schwarz Criterion. The sample periods vary based on the date of the initial release of each currency up to 31 December 2019. Results indicate the Power ARCH (PARCH) is the best model for Bitcoin and Litecoin, Threshold ARCH (TARCH) model is the best for Ethereum, Ripple, and Litecoin, and the EGARCH model is for Tether. Each model shows a different day of the week effects on each currency.
Serge Djoudji Temkeng, Achille Dargaud Fofack
The aim of this paper is to find out if the COVID-19 outbreak in the USA has a robust impact on the prices of cryptocurrencies. Inspired by the literature related to the determinants of cryptocurrency prices and based on data availability, six potential determinants of cryptocurrency prices and five proxies for the COVID-19 outbreak were selected. The impact of the COVID-19 outbreak was tested using two approaches of extreme bounds analysis and the robustness of our findings was further checked with different cryptocurrencies (Bitcoin, Ethereum, Litecoin and Bitcoin Cash). Our results show that new deaths from the COVID-19 have a robust positive impact on the price of cryptocurrencies while the impact of new confirmed cases, total cases, and total deaths is not robust. In line with previous studies, it is also found that economic uncertainty, stock, gold, and oil prices are robust determinants of the value of cryptocurrencies.