Follow blockchain research across journals, conferences, and preprint repositories.
2,329 results · page 44 of 98
StĂ©phane Goutte, Hoang-Viet Le, Fei Liu, HansâJörg von Mettenheim
No abstract is available for this record.
Zewen Wu
No abstract is available for this record.
Yongge Wang
No abstract is available for this record.
Akanksha Jalan, Roman Matkovskyy
No abstract is available for this record.
Yujun Liu, Zhongfei Li, Ramzi Nekhili, Jahangir Sultan
No abstract is available for this record.
Henrik Axelsen, Ulrik Terp Rasmussen, Johannes Rude Jensen, Omri Ross · 5 authors
The promising markets for voluntary carbon credits are faced with crippling challenges to the certification of carbon sequestration and the lack of scalable market infrastructure in which companies and institutions can invest in carbon offsetting. This amounts to a funding problem for green transition projects, such as in the agricultural sector, since farmers need access to the liquidity needed to fund the transition to sustainable practices. We explore the feasibility of mitigating infrastructural challenges based on a DLT Trading and Settlement System for green bonds. The artefact employs a multi-sharded architecture in which the nodes retain carefully orchestrated responsibilities in the functioning of the network. We evaluate the artefact in a supranational context with an EU-based regulator as part of a regulatory sandbox program targeting the new EU DLT Pilot regime. By conducting design-driven research with stakeholders from industrial and governmental bodies, we contribute to the IS literature on the practical implications of DLT.
Byounghyo Lim, Sol Kim, Ingoo Han
This study analyzes the role of Bitcoin as an investment asset in a global multi-asset portfolio. For portfolio construction, we use a risk-based portfolio strategy that excludes estimates of future expected returns. We derive the optimal asset allocation ratio of Bitcoin included in the global multi-asset portfolio and compare the performance with the general portfolio. We find that the average investment weight of Bitcoin in the portfolio is 1.8%, and the portfolio containing Bitcoin shows superior investment performance compared to the portfolio without Bitcoin.
Yiran Wang
With the advent of 2022, the impact of the COVID-19 pandemic has weakened, the US labor market has recovered, and inflation has been severe, creating the conditions for the Fed to tighten its policies. At the same time, cryptocurrencies as a hot topic in recent years; ETH is one of the most popular cryptocurrencies in the market; this article aims to assess the impact of the Fed's raised interest rates on the yield and volatility of cryptocurrency Ethereum (ETH) based on data on the ETH price and the US dollar/CNY exchange rate since 2022. And further, simulate the impact on the overall cryptocurrency market. This paper constructs VAR and ARMA-GARCH models to analyze ETH returns and volatility variations. The results of these models suggest that the exchange rate rise triggered by the Fed's rate hike has had a negative impact on ETH yields and increased the volatility of its returns. Further, this article recommends that investors should adjust their portfolios according to their risk appetite in an uncertain market environment.
Phuvadon Wuthisatian
This paper investigates the performance of cryptocurrencies and market indices. Using the dynamic conditional correlation (DCC) model, the result shows that cryptocurrencies and market indices, contrary to much of the literature, tend to move in the same direction, resulting in little or no benefits in portfolio management. Dividing into the sub-sample period, cryptocurrencies have moved even more strongly with market indices during the recent period after the COVID-19 pandemic, indicating the possibility of no hedging benefit. This paper shows that inclusion of cryptocurrency in a portfolio increases the return as well as volatility, as the risk-adjusted return does not show any sign of improvement. A portfolio comprising the FTSE 100 Index seems to receive the greatest benefit of including cryptocurrencies as the risk-adjusted performance improves.
Srinivasa Raghuram Daita, Shruti Bhargava Choubey, Abhishek Choubey
Cryptocurrency is a kind of virtual currency that came into existence with the recent advancement of technology in finance. It is used to complete transactions in a secure way by using the techniques of cryptography. This virtual currency is created with the help of block chain technology. In many countries, the transactions using cryptocurrency are not legalised by the banks. Some of the most popular cryptocurrencies are Bitcoin, Dogecoin, Litecoin, etc. The value of each cryptocurrency keeps varying from time to time. In this research paper, we build a data analytics model of the various cryptocurrency and also a machine learning model using the LSTM (Long Short-Term Memory) algorithm to forecast the value of a certain cryptocurrency on a particular day. This paper uses a web application called Yahoo Finance(yfinance) which has all the details of the live stock market and this acts as a source of dataset for this paper. Also, various python packages such as numpy, pandas, tensorflow, seaborn, matplotlib, etc. are used for building a model. The LSTM algorithm makes use of RNN (Recurrent Neural Network) which is powerful to model data sequencer because it has an internal memory state to store the past seen data. The proposed LSTM algorithm has a 98% accuracy which outperforms the accuracy of other existing models.
JosĂ© LuĂs Miralles QuirĂłs, MarĂa del Mar Miralles QuirĂłs
Since the formulation of the Efficient Market Hypothesis, countless studies have been developed that try to either prove or refute it. Event studies, analysing the impact of different events on asset prices, are one of the most important research fields but there is a lack of evidence on cryptocurrencies. For that reason, we analyse the existence of over- and under- reaction effects on Bitcoin after hourly price shocks defined by filter sizes. We also do this using three alternative approaches. Our results show clear evidence of overreaction after negative shocks. We also observe that these overreactions tend to be greater as more hours pass after the event, with those that occur between 6 and 24 hours after the event being especially important. These results have important economic implications because they show that investors would be able to develop a profitable trading strategy simply by focusing on investing after negative shocks.
Feng Jin, Jingwei Li, Yi Xue
No abstract is available for this record.
Xiaoquan Jiang, IvĂĄn M. RodrĂguez, Qianying Zhang
Abstract Based on asset pricing theory, we posit and find that equity markets and cryptocurrency markets share a common fundamental. Our cointegration tests show that the most important asset pricing primitive, consumption, can serve as the common fundamental. We further show that additional macroeconomic factors, as well as uncertainty and sentiment, all play a role in explaining the deviation from fundamentals. To understand the linkage between equity markets, cryptocurrency markets, and the macroeconomy, we suggest the following three channels: (i) portfolio allocation decisions, (ii) intermarket order flows, and (iii) technological adaption expectations.
Vandana Dangi
The emergence of cryptocurrencies futures market is an innovative platform for prudent investors to hedge risk contained in their portfolio. However, the dicey environment of cryptocurrencies and mandatory requirement of Ind AS 39 has aroused the need for estimating hedging effectiveness of their futures market. This treatise is an attempt to investigate the hedging effectiveness of Bitcoin, Ethereum, XRP and Bitcoin Cash covering the period from June 2018 to May 2022. The interconnectedness of their spot and futures markets is initially studied using Johansen cointegration test, dynamic conditional correlation model, vector error correction model and block exogeneity Wald test. Their empirical results indicate interconnectedness in these markets having significant long-term relationship; persistent volatility correlations; significant unidirectional long-term causality from futures to spot; and bidirectional short-term causality in all cryptocurrencies. So, investors can hedge their risk by engaging position in cryptocurrenciesâ futures. The OLS, VECM, GARCH and TARCH methodologies are applied to estimate static optimal hedge ratios and their estimates indicate that all cryptocurrencies have negative and significant ratios except XRP. The symmetric as well as asymmetric diagonal VECH and diagonal BEKK methodologies are applied to estimate dynamic-hedge ratios and their estimates depict negative mean dynamic-hedge ratios of all cryptocurrencies except XRP. These estimations imply that investors having long position in spot contracts of Bitcoin, Ethereum and Bitcoin Cash should hedge by taking short position in their futures contracts, respectively. However, XRP investors should hedge by taking long position in XRP future contracts. The empirical results clearly indicate the outperformance of static hedge strategies over dynamic hedge strategies as variance reduction framework of Ederington favours static OLS hedge strategy and the riskâreturn framework of Howard and DâAntonio favours static VECM hedge strategy for all cryptocurrencies. So, the long-run considerations have played a more crucial role as compared to short-run information. These findings may guide investors having different objective functions in understanding the effectiveness of different hedge strategies and their usage for achieving their objective functions. Policymakers, treasurers and auditors may also be benefitted from the insights provided in the present treatise on different estimation methodologies for hedging effectiveness.
J M de Almeida, Tiago Gonçalves
Our study collected and synthetized the existing knowledge on portfolio diversification, hedge, and safe-haven properties in cryptocurrency investments. We sampled 146 studies published in journals ranked in the Association of Business Schools 2021 journals list, considering all fields of knowledge, and elaborated a systematic literature review along with a bibliometric analysis. Our results indicate a fast-growing literature evidencing cryptocurrenciesâ ability to hedge against stocks, fiat currencies, geopolitical risks, and Economic Policy Uncertainty (EPU) risk; also, that cryptocurrencies present diversification and safe-haven properties; that stablecoins reveal unstable peg with the US dollar; that uncertainty is a determinant for cryptocurrency returns. Additionally, we show that investors should consider Gold, along with the European carbon market, CBOE Bitcoin futures, and crude oil to hedge against unexpected movements in the cryptocurrency market.
Pradipta Kumar Sahoo, Dinabandhu Sethi
Abstract Cryptocurrencies have emerged as an important investment avenue in the past few years. Investors are increasingly interested in these currencies amid surging financial returns. In this context, understanding market efficiency of cryptocurrency has become very crucial for investors and academicians. The priceâvolume framework is a popular approach in financial economics to understand the market efficiency of stocks in the stock markets. Therefore, this article examines the market efficiency of cryptocurrencies through priceâvolume framework to understand whether crypto market is predictable. Towards this objective, data on both return and trading volume (TV) of the top eight cryptocurrencies are used for the period 8 August 2015â20 October 2022. As an empirical method, both linear and nonâlinear causality models are used to validate the hypothesis. Our results confirm that TV cannot predict the cryptocurrencies' return, thereby validating the market efficiency hypothesis. Furthermore, we divide the sample according to the structural break period. The result from the postâbreak period analysis also confirms the presence of market efficiency in the recent period for all currencies, barring XRP, XMR and DASH.
Reza Nourmohammadi, Mahdi Arabian, Masoumeh Ghorbanpour, Mohammad M. Nazemi · 5 authors
As a cutting-edge technology, non-fungible tokens (NFT) have attracted a great deal of attention since 2021. Considering the numerous applications of these non-interchangeable digital assets in various industries and their tradability, NFTs have become an important element of many investorsâ portfolios. Therefore, in order to evaluate NFTs and determine their main value, different tools must be used. The purpose of this study is to understand the dominant factors that influence the valuation of NFT assets. The purpose of this paper is to present a novel methodology for constructing a utility valuation model for NFTs as a whole. We will be able to analyze and diagnose the dynamics and performance of NFT markets using this model. We developed three models for scoring NFTs in this study, which can be used to speed up the evaluation process in three different dimensions.
Alin ISTOCESCU
The aim of the International Conference "Economic Scientific Research - Theoretical, Empirical and Practical Approaches"- ESPERA, initiated in 2013 by the "Costin C. KiriÈescu" National Institute for Economic Research (NIER), is to present and evaluate the economic scientific research portfolio, to argue and substantiate the Romanian development strategies - including European and global best practices. The 2021 edition of the Conference will be held on 9th -10th December, under the title: "The crisis after the crisis. When and how the New Normal will be". The event scientific program addresses a wide diversity of themes, bringing together researchers from all NIER institutes and centres, members of the Romanian Academy, Romanian academic researchers and also guests from other countries. Researchers are encouraged to present articles on economic scientific research that they have focused, as much as possible, on paradigm shifts for the world after the COVID-19 crisis, since some deep and long-lasting changes are expected building up to a "New Normal". Singular relevant aspects could be related to complete digitalization and digital sovereignty, people and workforce management, virtual training and reskilling, digital currency, de-carbonization in all production processes, supply chains traceability, cybersecurity, automation, artificial intelligence and machine learning,
Antonio DĂaz, Carlos Esparcia, Diego HuĂ©lamo
This paper empirically assesses the ability of three putative stablecoins (two dollar-backed, Tether and USD Coin; and one gold-backed, Digix Gold) to mitigate the risk of facing severe losses (downside risk) of a traditional cryptocurrency portfolio. There are institutional features that induce cryptoinvestors to use stablecoins as diversifiers instead of withdrawing dollars or adding assets traditionally considered as safe havens, such as gold, crude oil, etc. Stablecoins, however, are not as stable as their name and collateralized peg suggest. A monthly rebalance experiment is conducted over an out-of-sample period considering higher order conditional moments when dynamically measuring the tail risk of cryptocurrency portfolios. The empirical evidence shows that the low conditional correlations of dollar-backed stablecoins with cryptocurrency portfolios make them particularly suitable as a hedge for crypto investors. It also shows that all stablecoins considered have high diversification capacities by systematically reducing portfolio tail risk.
Yuchen Dai
Covid-19 has had a significant impact on financial markets around the world, and various countries have adopted their methods to combat the impact of Covid-19 on equity markets.And because of Covid-19, the market share of the cryptocurrency market is increasing rapidly.This article focuses on how exchange rate changes caused by the Fed's interest rate hike affected the cryptocurrency market after the epidemic.The article uses ARMA-GARCH and VAR models to analyze the future change of the cryptocurrency market after 2022 and how an increase in interest rate affects cryptocurrency market volatility.Furthermore, the model predictions have not been found that the interest rate increase in early 2022 has produced volatility in the cryptocurrency market.Compared to traditional equity markets or real estate markets.Cryptocurrencies are not responsive to government intervention.
Dimple Bhojwani, Samik Shome
Major academic research related to cryptocurrencies has been developed by technologists who mainly focus on feasibility and security. Research in the area of economics and finance has provided limited insight in guiding investors to profit from cryptocurrencies, however, this article proposes a framework that depicts how rational investing can be achieved with the help of historical information published by cryptocurrency projects. Trading strategies based on fundamentals can generate economically significant gains relative to a buy-and-hold position.
Chung-Chi Chen, HenâHsen Huang, Hiroya Takamura, Makoto P. Kato · 5 authors
In this article, we provide an overview of ACM TWEBâs special issue, Financial Technology on the Web . This special issue covers diverse topics: (1) a new architecture for leveraging online news to investment and risk management, (2) a cross-platform analysis of the post quality and usersâ behaviors, and (3) an empirical study on disentangling decentralized finance compositions. In addition to a guide for the special issue, we also share a brief opinion on the future of financial technology on the Web.
Zhuhua Jiang, Walid Mensi, SeongâMin Yoon
This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perronâs structural break test, InclĂĄn and Tiaoâs iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Studentât distribution fits the cryptocurrency marketâs price dynamics well.
Luca Vincenzo Ballestra, Andrea Molent, Graziella Pacelli
Over the last years, the world of cryptocurrencies has undergone a tumultuous development, mostly characterized by speculative behaviors, and thus one might argue that it does not satisfy the Efficient Market Hypothesis. Since the efficiency of a financial market can be assessed by checking whether the times series of the assets traded on it are persistent/anti-persistent, we investigate the presence of memory in the price of seven among the most important cryptocurrencies. To this aim, we employ an original approach based on two fractional models, namely the geometric fractional Brownian motion and the geometric mixed fractional Brownian motion, which are tested against the Markovian geometric Brownian motion to detect the presence of memory. The above fractional models are estimated by employing an innovative maximum likelihood procedure that exploits the Toeplitz structure of the covariance matrix of log-returns. The null assumption of absence of memory in the time series is tested based on confidence ellipses for the estimated parameters. We validate the proposed procedure on artificial data and we apply it to the historical series of crypto-prices. Results suggest the presence of memory effects only for some of the considered cryptocurrencies. A possible explanation of such a persistence/anti-persistence phenomenon is provided.