Blockchain Papers

Zi‐Yi Guo

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.

Walid Mensi, Khamis Hamed Al‐Yahyaee, Idries Mohammad Wanas Al-Jarrah, Xuan Vinh Vo · 5 authors

No abstract is available for this record.

Rocco Caferra, Gabriele Tedeschi, Andrea Morone

No abstract is available for this record.

Parthajit Kayal, Purnima Rohilla

No abstract is available for this record.

Jeffrey Chu, Stephen Chan, Yuanyuan Zhang

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.

Mohamed Fakhfekh, Ahmed Jeribi, Ahmed Ghorbel, Néjib Hachicha

Purpose In a first place, the present paper is designed to examine the dynamic correlations persistent between five cryptocurrencies, WTI, Gold, VIX and four stock markets (SP500, FTSE, NIKKEI and MSCIEM). In a second place, it investigates the relevant optimal hedging strategy. Design/methodology/approach Empirically, the authors examine how WTI, Gold, VIX and five cryptocurrencies can be applicable to hedge the four stock markets. Three variants of multivariate GARCH models (DCC, ADCC and GO-GARCH) are implemented to estimate dynamic optimal hedge ratios. Findings The reached findings prove that both of the Bitcoin and Gold turn out to display remarkable hedging commodity features, while the other assets appear to demonstrate a rather noticeable disposition to act as diversifiers. Moreover, the results show that the VIX turns out to stand as the most effectively appropriate instrument, fit for hedging the stock market indices various related refits. Furthermore, the results prove that the hedging strategy instrument was indifferent for FTSE and NIKKEI stock while for the American and emerging markets, the hedging strategy was reversed from the pre-cryptocurrency crash to the during cryptocurrency crash period. Originality/value The first paper's empirical contribution lies in analyzing emerging cross-hedge ratios with financial assets and compare hedging effectiveness within the period of crash and the period before Bitcoin crash as well as the sensitivity of results to refits choose to compare between short term hedging strategy and long-term one.

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.

Xuejun Jin, Keer Zhu, Xiaolan Yang, Shouyang Wang

No abstract is available for this record.

Chang‐Che Wu, Chang‐Che Wu, Shu-Ling Ho, Chih‐Chiang Wu · 5 authors

No abstract is available for this record.

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.

Onur Polat, Eylül Kabakçı Günay

Purpose The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper implements the frequency connectedness approach of Barunik and Krehlik (2018) and to measure short-, medium- and long-term connectedness between realized volatilities of cryptocurrencies. Additionally, this paper analyzes network graphs of directional TO/FROM spillovers before and after the announcement of the COVID-19 pandemic by the World Health Organization. Design/methodology/approach In this study, we examine the volatility connectedness among eight major cryptocurrencies by the virtue of market capitalization by using the frequency connectedness approach over the period July 26, 2017 and October 28, 2020. To this end, this paper computes short-, medium- and long-cycle overall spillover indexes on different frequency bands. All indexes properly capture well-known events such as the 2018 cryptocurrency market crash and COVID-19 pandemic and markedly surge around these incidents. Furthermore, owing to notably increased volatilities after the official announcement of the COVID-19 pandemic, this paper concentrates on network connectedness of volatility spillovers for two distinct periods, July 26, 2017–March 10, 2020 and March 11, 2020–October 28, 2020, respectively. In line with the related studies, major cryptocurrencies stand at the epicenter of the connectedness network and directional volatility spillovers dramatically intensify based on the network analysis. Findings Overall spillover indexes have fluctuated between 54% and 92% in May 2018 and April 2020. The indexes gradually escalated till November 9, 2018 and surpassed their average values (71.92%, 73.66% and 74.23%, respectively). Overall spillover indexes dramatically plummeted till January 2019 and reached their troughs (54.04%, 57.81% and 57.81%, respectively). Etherium catalyst the highest sum of volatility spillovers to other cryptocurrencies (94.2%) and is followed by Litecoin (79.8%) and Bitcoin (76.4%) before the COVID-19 announcement, whereas Litecoin becomes the largest transmitter of total volatility (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%). Except for Etherium, the magnitudes of total volatility spillovers from each cryptocurrency notably increase after – COVID-19 announcement period. The medium-cycle network topology of pairwise spillovers indicates that the largest transmitter of total volatility spillover is Litecoin (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%) before the COVID-19 announcement. Etherium keeps its leading role of transmitting the highest sum of volatility spillovers (89.4%), followed by Bitcoin (88.9%) and Litecoin (88.2%) after the COVID-19 announcement. The largest transmitter of total volatility spillovers is Etherium (95.7%), followed by Litecoin (81.2%) and Binance Coin (75.5%) for the long-cycle connectedness network in the before-COVID-19 announcement period. These nodes keep their leading roles in propagating volatility spillover in the latter period with the following sum of spillovers (Etherium-89.5%, Bitcoin-88.9% and Litecoin-88.1%, respectively). Research limitations/implications The study can be extended by including more cryptocurrencies and high-frequency data. Originality/value The study is original and contributes to the extant literature threefold. First, this paper identifies connectedness between major cryptocurrencies on different frequency bands by using a novel methodology. Second, this paper estimates volatility connectedness between major cryptocurrencies before and after the announcement of the COVID-19 pandemic and thereby to concentrate on its impact on the cryptocurrency market. Third, this paper plots network graphs of volatility connectedness and herewith picture the intensification of cryptocurrencies due to a major financial distress event.

Yuanyuan Chen

No abstract is available for this record.

Achraf Ghorbel, Ahmed Jeribi

Purpose In this paper, we investigate empirically the time-frequency co-movement between the recent COVID-19 pandemic, G7stock markets, gold, crude oil price (WTI) and cryptocurrency markets (bitcoin) using both the multivariate MSGARCH models. Design/methodology/approach This paper examines the relationship between the volatilities of oil, Chinese stock index and financial assets (cryptocurrency, gold, and G7 stock indexes), for the period January 17th 2020 to December 10th 2020. It tests the presence of regime changes in the GARCH volatility dynamics of bitcoin, gold, Chinese, and G7 stock indexes as well as oil prices by using Markov–Switching GARCH model. Also, the paper estimates the dynamic correlation and volatility spillover between oil, Chinese and financial assets by using the MSBEKK-GARCH and MSDCC-GARCH models. Findings Overall, we find that all variables display a strong volatility concentrated in the first four months of Covid-19 outbreak. The paper conducts different backtesting procedures of the 1% and 5% Value-at-Risk forecasts of risk. The results find that gold has the lowest VaR. However, the Canadian and American indices have the highest VaR, for respectively 1% and 5% confidence level. The estimation results of MSBEKK-GARCH prove the volatility spillover between Chinese index, oil and financial assets. Although, the past news about shocks in the Chinese index significantly affects the current conditional volatility of financial assets. Moreover, for the high regime, the correlation increased between Chinese and G7 stock indexes which proving the contagion effect of the COVID-19 pandemic. On the contrary, the correlation decreased between Chinese-gold and Chinese-bitcoin, which confirming that gold and bitcoin can be considered as an alternative hedge for some investors during a crisis. During the COVID-19 pandemic, the correlations for the couples oil-gold and oil-bitcoin peaked. Contrary to gold, bitcoin cannot be considered as a safe haven during the global pandemic when investing in crude oil. Originality/value In contrast, comparative analysis in terms of responses to US COVID-19 pandemic, the US Covid-19 confirmed cases have relative higher impact on the co-movement in WTI and bitcoin. This paper confirms that gold is a safe haven during the COVID19 pandemic period.

Salim Lahmiri, Stelios Bekiros, Anastasia Giakoumelou

Blockchain is a related FinTech asset but it is not the same technology. Basically, Blockchain is a decentralized and distributed digital ledger used to record Bitcoin transactions. The goal of this work is to employ multi-scale analysis to examine self-similarity in EDC Blockchain digital asset. Specifically, market technical data are examined; namely, open, high, low, and close. The resulting generalized Hurst exponent (GHE) estimates revealed that all Blockchain technical indicators exhibit multi-scale dynamics. In addition, short and long dynamics are different. It is concluded that market technical indicators associated with Blockchain provide valuable information for traders.

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.