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2,964 results · page 81 of 124
Zhuorui Zhang, Hong‐Ning Dai, Junhao Zhou, Subrota Kumar Mondal · 6 authors
No abstract is available for this record.
A. Melki, Nourhaine Nefzi
No abstract is available for this record.
Samet Günay, Kerem Kaşkaloğlu, Shahnawaz Muhammed
This study examines the interaction of Bitcoin with fiat currencies of three developed (euro, pound sterling and yen) and three emerging (yuan, rupee and ruble) market economies. Empirical investigations are executed through symmetric, asymmetric and non-linear causality tests, and Markov regime-switching regression (MRSR) analysis. Results show that Bitcoin has a causal nexus with Chinese yuan and Indian rupee for price and various return components. The MRSR analysis justifies these findings by demonstrating the presence of interaction in contractionary regimes. Accordingly, it can be stated that when markets display a downward trend, appreciation of the Chinese yuan and Indian rupee positively and strongly affects the value of Bitcoin, possibly due to the market timing. The MRSR analysis also exhibits a transition from a tranquil to a crisis regime in March 2020 because of the pandemic. However, a shorter duration spent in the crisis regime in 2020 indicates the limited and relatively less harmful effect of the pandemic on the cryptocurrency market when compared to the turmoil that occurred in 2018.
Zied Ftiti, Waël Louhichi, Hachmi Ben Ameur
No abstract is available for this record.
Pavel Ciaian, d’Artis Kancs, Miroslava Rajčániová
We studied the extent to which bitcoin blockchain security permanently depends on the underlying distribution of cryptocurrency market outcomes using daily blockchain and bitcoin data for 2014–2019 and employing the autoregressive-distributed lag (ARDL) approach. We tested three equilibrium hypotheses: (i) sensitivity of the bitcoin blockchain to mining reward, (ii) security outcomes of the bitcoin blockchain and the proof-of-work cost, and (iii) the speed of adjustment of the bitcoin blockchain security to deviations from the equilibrium path. Our results suggest that bitcoin price and mining rewards were intrinsically linked to bitcoin security outcomes.The bitcoin blockchain security’s dependency on mining costs was geographically differenced – it was more significant for the global mining leader China than for other world regions. Bitcoin blockchain security tended to revert relatively fast to its equilibrium security level after the input or output of price shocks.
Samia Nasreen, Aviral Kumar Tiwari, Seong‐Min Yoon
This paper examines interlinkages and hedging opportunities between nine major cryptocurrencies for the period between 30 September 2015 and 4 June 2020, which notably includes the coronavirus disease 2019 (COVID-19) outbreak lasting from early 2020 through the end of the sample period. The results of dynamic conditional correlation (DCC) analysis using a minimum connectedness approach show a high degree of correlation between cryptocurrencies throughout the sample period. However, the correlations reach their minimum values during the COVID-19 pandemic, which indicates that cryptocurrencies acted as a hedge or safe haven during the stressful period of the COVID-19 pandemic. The weight of cryptocurrencies was significantly reduced and their hedging effectiveness varied greatly during the pandemic, which indicates that investors’ preferences changed during the COVID-19 period.
Pierre Venter, Eben Maré
In this paper, the Heston–Nandi futures option pricing model is applied to Bitcoin futures options. The model prices are compared to market prices to give an indication of the pricing performance. In addition, a multivariate Bitcoin futures option pricing methodology based on a multivatiate GARCH model is developed. The empirical results show that a symmetric model is a better fit when applied to Bitcoin futures returns, and also produces more accurate option prices compared to market prices for two out of three expiry dates considered.
Khurram Shehzad, Faik Bilgili, Umer Zaman, Emrah Koçak · 5 authors
No abstract is available for this record.
Natalia Diniz-Maganini, Eduardo Henrique Diniz, Abdul Rasheed
No abstract is available for this record.
Zartashia Hameed, Khuram Shafi, Samina Nawab
The worth of digital currencies is increasing due to its proposed advantages and profits. Though decentralized, these digital currencies can be bought with digital wallets using cryptocurrency platform. Efficient Market Hypothesis (EMH) suggests fundamentals for understanding of financial markets however the opponents believe that this theory is incompetent in explaining the functioning of the markets. EMH is not a perfect model nevertheless it provides a concrete base for the analysis of capital markets. EMH’s weak version is utilized for this study. This research compares three top cryptocurrencies- Bitcoin, Ethereum and Litecoin to analyze their long-range memory effect to check the market efficiency and also to estimate the volatility for further investments in different cryptocurrencies. Generalized Hurst exponent methodology is applied to examine long range memory in selected cryptocurrencies market. Daily data from 17th September 2015 till 17th October 2018 is used in this study. It was found that: (i) Long memory exists in the selected cryptocurrencies; (ii) Ethereum market is more persistent than Bitcoin and Litecoin as its Hurst exponent is more than the other cryptocurrencies. These findings can be a source of assistance for the policy makers and investors while making prudent decisions regarding investment in emerging cryptocurrencies market.
Parthajit Kayal, Purnima Rohilla
No abstract is available for this record.
Alex de Vries, Ulrich Gallersdörfer, Lena Klaaßen, Christian Stoll
No abstract is available for this record.
Mustafa Disli, Ruslan Nagayev, Kinan Salim, Siti Kholifatul Rizkiah · 5 authors
No abstract is available for this record.
Mert Baran Tunçel, Yaşar ALPTÜRK, Mehmet Akif ALTUNAY, İ̇smail BEKCİ
Bu araştırmanın amacı, Bitcoin fiyatları ile BIST100 endeksi arasındaki nedensellik ilişkisini tespit etmeye çalışmaktır. Araştırmada19 Temmuz 2010 ile 10 Ocak 2020 arasındaki dönemleri kapsayan Bitcoin fiyatları ve BIST100 endeksi günlük verileri(2452 Gözlem) kullanılmıştır. Serilerin durağanlığını test etmek için yapısal kırılmaları göz ardı etmeyen Lee Strazicich birim kök testi kullanılmıştır. Daha sonra Toda-Yamamoto testi ile değişkenler arasında nedensellik olup olmadığı, nedensellik varsa nedenselliğin yönünün ne olduğu tespit edilmeye çalışılmıştır. Toda-Yamamoto(1995) nedensellik testi sonuçlarına göre, BIST100 endeksi değişkeninden Bitcoin fiyatları değişkenine doğru ve Bitcoin fiyatları değişkeninden BIST100 endeksi değişkenine doğru %5 anlamlılık seviyesinde nedensellik ilişkisine rastlanılmamıştır.
Joseph J. French
We investigated the differential impacts of a new Twitter-based Market Uncertainty index (TMU) and variables for Bitcoin before and during the COVID-19 pandemic. Results showed that TMU is a leading indicator of Bitcoin returns only during the pandemic, and the effect of the TMU on Bitcoin’s conditional volatility is significantly greater during the pandemic. Furthermore, during the pandemic, the uncertainty content of people’s tweets is impacted by the highly salient Bitcoin market. Taken together, our results suggest that the information contained in virtual communities such as Twitter have a much larger impact on cryptocurrency markets following COVID-19.
Muhammad Abubakr Naeem, Imen Mbarki, Majed Alharthi, Abdelwahed Omri · 5 authors
COVID-19 has morphed from a health crisis to an economic crisis that affected the global economy through several channels. This paper aims to study the impact of COVID-19 on the time-frequency connectedness between Green Bonds and other financial assets. Our sample includes the global stock market, bond market, oil, USD index, and two popular hedging alternatives, namely Gold and Bitcoin, from May 2013 to August 2020. First, we apply the methodologies of Diebold and Yilmaz (International Journal of Forecasting, 2012, 28(1), 57–66) and Baruník and Křehlík (Journal of Financial Econometrics, 2018, 16(2), 271–296). Then, we estimate hedge ratios and hedge effectiveness of green bonds for other financial assets. Green bonds are found to have a great weight in the overall network, particularly strongly connected with the USD index and bond index. While the bi-directional relationship with USD persists during COVID, the connectedness with conventional bonds is also strengthened. Notably, we find a weak relationship between Green bonds and Bitcoin, both in the short and long run. As portfolio implications, Gold and USD have the highest hedge ratio, which is confirmed by the hedging effectiveness. In contrast, oil and stocks exhibit the lowest hedging effectiveness. Our findings imply that financial assets might have a heterogeneous relationship with green bonds. Furthermore, despite its infancy, it seems that the role of green bond during a crisis should not be ignored, as it can be a hedger for some assets, while a contagion amplifier during crisis times.
Lan-TN Le, Larisa Yarovaya, Muhammad Ali Nasir
No abstract is available for this record.
Julien Chevallier, Dominique Guégan, Stéphane Goutte
This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.
Sanaz Chamanara, S. Arman Ghaffarizadeh, Kaveh Madani
The cryptocurrency sector is increasingly integrated into the global financial system. The world’s transition to a digital economy, facilitated by major technological breakthroughs, has several benefits. But as the demand for exchanging and investing in digital currencies is growing , the world must pay careful attention to the hidden and overlooked environmental impacts of this growth. The dramatic increase in the price of Bitcoin (BTC) over the last year and the resulting global race for BTC mining is turning the cryptocurrency market turning into one of the world’s leading polluting sectors. Yet, our knowledge about the environmental footprints of mining BTC is very limited. To address this hap, this study provides the first estimates of the carbon, water and land footprints of BTC mining around the world.
Imran Yousaf, Shoaib Ali, Elie Bouri, Tareq Saeed
This study uses hourly data to analyse the return and volatility transmission of oil-gold and oil-Bitcoin pairs during the pre-COVID-19 and COVID-19 periods. The results show that the return transmissions vary across the two periods for both pairs. There is a unidirectional volatility spill-over from gold to oil in the pre-COVID-19 period, and from oil to gold during the COVID-19 period. There is a significant volatility spill-over from Bitcoin to oil during the pre-COVID-19 period, whereas no evidence of volatility spill-over between oil and Bitcoin is shown during the COVID-19 period. Based on optimal weights, investors should increase their investments in, (a) gold for a portfolio of oil-gold, and (b) Bitcoin for a portfolio of oil-Bitcoin during the COVID-19 period. All hedge ratios are higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. The results of hedging effectiveness reveal that the risk-adjusted returns can be improved by constructing a portfolio of oil-gold and oil-Bitcoin during both sample periods. Further results reveal that gold is a strong safe haven and a hedge for the oil market, while Bitcoin serves as a diversifier for the oil market during the COVID-19 period.
Serdar Neslihanoglu
This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.
Tim Leung, Theodore Zhao
In this study, we study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.
Ngô Thái Hưng
Purpose This study examines the inter-linkages between Bitcoin prices and CEE stock markets (Hungary, the Czech Republic, Poland, Romania and Croatia). Design/methodology/approach The dynamic contemporaneous nexus has been analyzed using both the multivariate DECO-GARCH model proposed by Engle and Kelly (2012) and quantile on quantile (QQ) methodology proposed by Sim and Zhou (2015). Our study is implemented using the daily data spanning from 6 September 2012 to 12 August 2019. Findings First, the findings show that the average return equicorrelation across Bitcoin prices and CEE stock indices are positive, even though it is found to be time-varying over the research period shown. Second, the Bitcoin-CEE stock market association has positive signs for most pairs of quantiles of both variables and represents a rather similar pattern for the cases of Poland, the Czech Republic and Croatia. However, a weaker and primarily negative connectedness is found for Hungary and Romania, respectively. Furthermore, the interconnectedness between the co-movements in the Bitcoin market and stock returns changes significantly across quantiles of both variables within each nation, indicating that the Bitcoin-stock market relationship is dependent on both the cycle of the stock market and the nature of Bitcoin price shocks. Practical implications The evidence documented in this study has significant implications for divergent economic agents, including global investors, risk managers and policymakers, who would benefit from a comprehensive knowledge of the Bitcoin-stock market relationship to build efficient risk-hedging models and to conduct appropriate policy reactions to information spillover effects in different time horizons. Originality/value This paper is the first study employing both the multivariate DECO-GARCH model and QQ methodology to shed light on the nexus between Bitcoin prices and the stock markets in CEE countries. The DECO model uses more information to compute dynamic correlations between each pair of returns than standard dynamic conditional correlation (DCC) models, declining the estimation noise of the correlations. Besides, QQ approach allows us to capture some nuanced features of the Bitcoin-stock market relationship and explore the interdependence in its entirely. Therefore, the main contribution of this article to the related literature in this field is significant. 研究目的 本研究旨在探討比特幣的價格與中東歐股市(匈牙利、捷克共和國、波蘭、羅馬尼亞和克羅地亞) 之相互聯繫. 研究設計/方法/理念 研究使用恩格爾與凱利(2012)(Engle and Kelly (2012)) 提出的多變量DECO-GARCH模型及Sim 與Zhou(2015)(Sim and Zhou ( 2015)) 研製的分位數-分位數方法來分析動態同期的聯繫。我們的研究使用由2012年9月6日至2019年8月12日期間取得的每日數據來進行. 研究結果 首先、研究結果顯示、跨比特幣價格與中東歐股價指數的平均回報當量關聯是正相關的，即使在研究期間被發現是隨時間而變化的。第二、比特幣與中東歐股市之聯繫在大多數兩變數分位數對而言出現正相關跡象，而且，這聯繫在波蘭、捷克共和國及克羅地亞而言表現一個頗相似的模式。唯就匈牙利而言、這聯繫則較弱、而羅馬尼亞則主要是負聯繫。研究結果亦顯示: 比特幣市場內的聯動與股票回報間之內在關聯會在每個國家內跨兩個變數的分位數而顯著地改變，這顯示比特幣-股市關係是取決於股市的週期和比特幣價格衝擊的本質. 實際的意義 本研究所記載的證據、對不同的經濟行為者而言極具意義 (這包括國際投資者、風險管理經理和政策制定者)，因他們會受惠於對比特幣-股市關係的全面認識，他們可建立有效的風險對沖模型、及在不同時間範圍對資訊溢出效應進行適當的政策反應. 研究的原創性/價值 本文為首個研究使用多變量DECO-GARCH模型和分位數-分位數(QQ)方法、來解釋比特幣價格與中東歐國家之股市的關係。這DECO模型使用比標準動態條件關係模型更多資訊，來計算每對回報間之動態關係，這能減少估測雜訊，而且，QQ方法讓我們可以取得比特幣-股市關係的一些細微特徵及全面地探索其相互依賴性。因此，本文的主要貢獻是在這學術領域內有關的文獻上.
Chiraz Karamti, Olfa Belhassine
No abstract is available for this record.