Blockchain Papers

Junming Zhao, Tianding Zhang

No abstract is available for this record.

Imran Yousaf, Mariya Gubareva, Тамара Теплова

Employing the vector auto-regression based on generalized forecast error variance decomposition, this paper investigates the connectedness of non-fungible tokens (NFTs) with precious and industrial metals and compares the results with those for conventional cryptocurrencies (CCCs). Our study scrutinizes separately the total static and the net dynamic spillovers of returns and volatilities from March 2018 to August 2021. We evidence that both, the total return and total volatility connectedness indices for the NFTs-metals framework are below the respective indices for the CCCs-metals framework, indicating new avenues for hedging and harvesting diversification benefits of NFT exposures. We provide empirical evidence that the NFTs are distinct from the CCCs not only in terms of the volatility spillovers, but in terms of the return spillovers too. In addition, we observe the decoupling in the net volatility spillovers between the precious and non-precious metals due to the COVID-19 meltdown. COVID-19 makes precious metals transmit volatility while industrial metals continue acting as net receivers of volatility shocks. Optimal weight and hedge ratios are presented for NFT-metal and crypto-metal pairs. These findings provide potential implications for investors and policy makers.

Pooja Kumari, Vasanthi Mamidala, Kavita Chavali, Abhishek Behl

No abstract is available for this record.

Ernest N. Biktimirov, Liana E. Biktimirova

No abstract is available for this record.

Hyeonoh Kim, Eojin Yi, Jooyoung Jeon, Taeyoung Park · 5 authors

No abstract is available for this record.

Anjali Yadav -, Akhilesh Kumar -

This paper explores the financial asset capabilities and hedge alternative properties of Bitcoin by investigating several aspects of its volatility in relation to Nifty50 and Indian exchange rates (USD/INR & EUR/INR). This study delves into the volatility dynamics of the returns of Bitcoin. An asymmetric GARCH model (E-GARCH) is used to examine whether Bitcoin may play crucial role in risk management and ideal for risk-averse investors in anticipation of leverage effect. This paper also examines Bitcoin as an investment and hedge alternative to Nifty50 and exchange rates. The findings suggest that Bitcoin does not attributes the properties of safe hedge and an investment alternative by Indian investors

Kia Jahanbin, Mohammed Ali Zare Chahooki

OBJECTIVES: With the expansion of social networks such as Twitter, many experts share their opinions on various topics. The opinions of experts, who are also known as influencers, can be very influential. Combining these tweets and the historical prices of cryptocurrencies makes it possible to predict their price trends accurately. A Hybrid of RoBERTa deep neural network and BiGRU has been used for Sentiment Analysis (SA). Sentiments of tweets can be of great help to investors to understand the future behavior of the market and manage the stock portfolio. Unlike the tweets that are only extracted using the cryptocurrency name hashtag, the tweets of this dataset have specialized opinions and can determine the market trend. DATA DESCRIPTION: The dataset created in this research concerns the opinions of more than 52 influencers (persons or companies) regarding eight cryptocurrencies. This dataset was collected through the Apify Twitter API for eight months, from February 2021 to June 2023. This dataset contains five Excel files and tweets, compound score, importance coefficient of each tweet, sentiment polarity, and historical prices of four cryptocurrencies: Bitcoin, Ethereum, Binance, and other information. These tweets cover the opinions of 52 influencers on more than 300 cryptocurrencies, although most comments are related to Bitcoin, Ethereum, and Binance. For this reason, three Excel files containing the historical prices of polarity and compound sentiment related to Bitcoin, Ethereum, and Binance cryptocurrencies have been placed separately in the dataset. The polarity of sentiment in these Excel shows the maximum number of polarities by applying the importance coefficient, which determines the dominant polarity of sentiment related to a particular day for the cryptocurrency.

Sera Şanlı, Mehmet Balcılar, Mehmet Özmen

No abstract is available for this record.

Adeyinka Adediran, Bola Babajide, Nataliia Osina

No abstract is available for this record.

Edosa Getachew Taera, Budi Setiawan, Adil Saleem, Andi Sri Wahyuni · 7 authors

This study investigates the volatility and external shock persistence within the financial and alternative assets markets during times of crises triggered by Covid-19 and the war in Ukraine. Univariate GARCH family models are used to capture the effect of financial turmoil caused by recent crises. Five different class of assets (which includes Islamic, ESG, Conventional, Crypto, FinTech, and commodities) have been chosen to represent a sample of the worldwide traditional financial market and alternative assets. The findings of this study revealed that almost all financial and alternative assets experienced an increase in volatility, except Bitcoin, across all observation periods. Islamic stock and ESG indexes exhibited high volatility before the Covid-19 outbreak. During the pandemic, all assets became more volatile. In addition, Islamic equities and ESG indexes showed relatively lower risk compared to conventional stocks and other alternative assets during the war. Multiple financial assets tend to be highly volatile during crises; however, global investors need to consider the advantages of incorporating Islamic stocks and ESG indexes as part of their investment portfolio innovation strategy, particularly in the presence of geopolitical risk.

Bilel Sanhaji, Julien Chevallier

Using the capital asset pricing model, this article critically assesses the relative importance of computing ‘realized’ betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the ‘pure’ systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include ‘realized’ versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails.

Giacomo di Tollo, Joseph Andria, Gianni Filograsso

Cryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of cryptocurrency and stock trends by using data from social online communities and platforms to assess their contribution in terms of predictive power. We extend recent developments in the field by exploiting a combination of stochastic neural networks (NNs), an extension of standard NNs, natural language processing (NLP) to extract sentiment from Twitter, and an external evolutionary algorithm for optimal parameter setting to predict the short-term trend direction. Our results point to good and robust accuracy over time and across different market regimes. Furthermore, we propose to exploit recent advances in sentiment analysis to reassess its role in financial forecasting; in this way, we contribute to the empirical literature by showing that predictions based on sentiment analysis are not found to be significantly different from predictions based on historical data. Nonetheless, compared to stock markets, we find that the accuracy of trend predictions with sentiment analysis is on average much higher for cryptocurrencies.

Meng Qin, Tong Wu, Xuecheng Ma, Lucian-Liviu Albu · 5 authors

No abstract is available for this record.

Mouna Aloui, Besma Hamdi, Aviral Kumar Tiwari, Ahmed Jeribi

Purpose This study aims to explore the impact of cryptocurrencies (Bitcoin, Ethereum, Monero and Ripple) on the gold, WTI, VIX index, G7 and the BRICS index before and during COVID-19. Design/methodology/approach This research analyzes the impact of cryptocurrencies (Bitcoin, Ethereum, Monero and Ripple) on the gold, WTI, VIX index, G7 and the BRICS index before and during COVID-19, using the quantile regression approach for the 2016–2020 period. In addition, to catch long- and short-run asymmetries of cryptocurrencies on aforementioned dependent variables, an asymmetric nonlinear co-integration (nonlinear autoregressive distributed lag [NARDL]) approach is applied. Findings The result of the quantile regression shows that in a high market, which corresponds to the 90th quantile, the FTSE MIB, CAC40, SSE, BSE 30, and BVSP stock market showed a statistically insignificant negative coefficient, on the Bitcoin price. In a middle and low markets, which correspond to the 0.2, 0.3 and 0.5th quantiles, the BVSP, FTSE MIB, S&P/TSX, SSE and Nikkei stock markets show statistically significant and positive on Bitcoin. Evidence from the NARDL shows a statistically significant positive impact of cryptocurrencies on the gold, WTI, VIX index, G7 and BRICS indices before and during COVID-19 pandemic. Originality/value These results can provide investors with valuable analysis and information and help them make the best decisions and adopt the best strategies. Therefore, future investigations may concentrate and examine the monetary and governmental policies to be adapted to face the COVID-19 pandemic’s dangerous effects on both the society and the economy. For this reason, investors should take this into account when making their asset allocation decisions. Moreover, the portfolio managers, such as index funds, may consider few eligible cryptocurrencies for their inclusion into the portfolio. However, the speculators present in both stock and crypto markets may opt for a spread strategy to improve their portfolio returns.

Onur Polat

Purpose This study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques. Design/methodology/approach This work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios. Findings This study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs. Research limitations/implications This study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al ., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk. Originality/value The contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

Avinash Malik

Abstract Bitcoin (BTC) perpetual futures contracts are highly leveraged speculative trading instruments with daily market trading of $45 Billion. BTC perpetual futures are derivative contracts, which depend upon the underlying BTC SPOT (current) price. Pricing perpetual futures fairly is hard, using traditional arbitrage arguments, because of the volatile nature of the so called funding rate, which is used as the replacement of risk free rate in the Cryptocurrency market. This work presents a novel technique for pricing BTC futures contracts using conditional volatility and mean models. Intra‐day high‐frequency futures' return volatility and mean are modelled using different ML and econometric techniques. A comparison is made using statistical measures to find the model that best captures the intra‐day conditional mean and volatility. Exponential generalized autoregressive conditional heteroskedasticity is shown to be an almost unbiased predictor of intra‐day volatility, while a constant autoregressive moving average (0, 0) model best captures the conditional mean of the returns. A market directional high frequency trading algorithm is developed using the volatility and mean models. The algorithm first prices the futures contract at some future point of time using the volatility and mean regression models. Next, the slope between the current futures price and the expected price are used to predict the market direction. A long or short position is taken depending upon the expected market direction movement. Extensive back‐testing results show absolute returns of 1500%–8000% depending upon the transaction fees and leverage used. On average, the market direction is predicted correctly 85% of the time by the best model. Finally, the trading technique is market neutral, in that it gives large positive returns, with low SD, in both bull and bear markets.

Mohsin Sadaqat, Hilal Anwar Butt

No abstract is available for this record.

Sharda Kumari, Vipin Kumar, A. Sharmila, C. Ravindra Murthy · 6 authors

Cryptocurrencies have experienced rapid growth and gained popularity worldwide. As a result, social media platforms have become an important source of information for investors and traders seeking to make decisions regarding cryptocurrencies. However, due to the sheer volume of data and the lack of effective analysis techniques, it can be challenging to identify key patterns and trends within social media forums. In this research paper, a blockchain based methodology is proposed for analysis of social media forums for cryptocurrency phase shifts. Specifically, we use sentiment analysis and topic modeling techniques to identify key themes and trends within cryptocurrency social media forums, and then use blockchain technology to verify and authenticate the data. Our results demonstrate that our approach is effective in identifying phase shifts in cryptocurrency markets. In recent years, social media forums have become a hub for discussions on cryptocurrency. The vast amount of data generated in these forums can be analyzed to predict the price shifts of different cryptocurrencies. However, traditional analysis methods may not be efficient enough to extract relevant information from this vast data. Therefore, this research study proposes a blockchain-based analysis approach to social media forums for cryptocurrency phase shifts. The proposed approach uses blockchain to ensure data immutability, and smart contracts to execute the analysis. The results obtained from the analysis can be used for prediction of price shifts of different crypto-currencies.

Meduri V N S S R K Sai Somayajulu, Bonthu Kotaiah

Deep-learning and machine-learning algorithms have recently become a prominent research topic for forecasting the price of cryptocurrencies. Some research indicates that deep learning models are incapable of accurately and promptly predicting daily cryptocurrency prices, whereas other research compares the efficacy of various models. Such techniques include machine learning, deep learning, and statistical models, among others. Several studies have devised hybrid approaches that combine novel methodologies in an effort to enhance the accuracy of bitcoin price forecasts. Complex models of deep learning and interdependent relationships are examples of these modern methods. To further improve the quality of survey data, there are additional datasets that making frequent errors. The search results indicate that efforts are being made to better bitcoin price estimations using deep learning and hybrid methods.

Sasan Barak, Navid Parvini

Abstract Despite the growing literature in cryptocurrency forecasting and their price drivers, the relationship between their price and other financial time series is an ongoing matter of debate. This study proposes a three‐step methodology to cover these arguments. First, we conduct an ad hoc analysis using transfer entropy (TE) to study the causal relationship between Bitcoin (BTC) returns and a vast array of financial time series. Then, we utilize variables with a significant amount of information flow toward BTC returns to forecast multi‐step‐ahead BTC returns. Finally, we use explainable artificial intelligence post hoc analysis methods to discover the contribution of each input feature to the overall forecasting. The results indicate a significant change in the information flow pattern in the first days of the COVID‐19 pandemic outbreak. Additionally, our proposed TE‐based feature‐selection method outperforms both benchmarks, a nonfeature‐selection model, and backward stepwise regression.

Zaghum Umar, Sun‐Yong Choi, Тамара Теплова, Tatiana V. Sokolova

Are green investments decoupled from the dirty investment such as the fossil fuel markets? We address this issue by extending the literature on environmental, social, and governance (ESG) assets by examining the dynamic relationship between fossil fuels and digital ESG assets proxied by green cryptocurrencies using the TVP-VAR(Time-varying parameter vector auto regression) spillover framework. Furthermore, we analyze the hedging attributes of green cryptocurrencies and fossil fuels in a minimum connectedness framework. The main findings are as follows: First, green cryptocurrencies are the main shock transmitters in all asset systems. Second, the dynamic connectedness between green cryptocurrencies and fossil fuels increased during the COVID-19 and Russia-Ukraine conflicts. Third, green cryptocurrencies have shown considerable hedging effectiveness against the fossil fuels. Our study has important implications for investors, regulators, and policy makers, such as shifting to green cryptocurrencies, regulation of carbon footprint, and promoting eco-friendly assets.

Saswat Patra, Neha Gupta

Cryptocurrencies have gained much attention in recent times with investors, speculators, and regulators showing a keen interest in the cryptocurrency markets. However, not much attention has been paid to quantifying their risk measures. This paper estimates the risk in the cryptocurrency markets using Value-at-Risk and Expected Shortfall. We use Johnsons Su distribution to model the innovations in the returns and present a comparative analysis of different fat-tailed and skewed distributions used in modeling the returns. The estimation takes into account endogenously determined structural breaks in the data. We employ several backtesting methodologies to test the efficacy of the forecasts. Empirical results show that the Johnson’s Su distribution gives exceptional results, and outperforms other fat-tailed distributions and the normal distribution, especially at the 1% (for long positions) and 99% levels (for short positions). Furthermore, our results are robust to different subsamples and the methodology employed (recursive or rolling window). Our results have clear policy implications for various market participants, regulators, and the government.

Abbas Valadkhani

Purpose This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail. Design/methodology/approach This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary. Findings The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin. Practical implications The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks. Originality/value The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.

Kais Ben-Ahmed, Saliha Theiri, Naziha Kasraoui

This research examines the impact of the coronavirus index on the returns and volatility of ten major cryptocurrencies during the COVID-19 pandemic. For this purpose, we applied a multivariate volatility GARCH model with an integrated dynamic conditional correlation (DCC) approach to daily cryptocurrency values observed data during the January-December, 2020 period. Moreover, we used the Granger causality test to study return-volume correlations. The findings indicate that cryptocurrency volatility declined after the World Health Organization declared on March 11, 2020, that the coronavirus was a pandemic. Unlike most of the relevant previous studies, we found that the COVID-19 crisis did not have a long-term effect on cryptocurrency returns and volatility but only presented a short-term effect. Our results have implications for investors who need to determine an optimal portfolio for a scenario other than the base.