Blockchain Papers

Isik Akin, Muhammad Zubair Khan, Affan Hameed, Kaouthar Chebbi · 5 authors

No abstract is available for this record.

Nguyễn Hoàng Nam

Cryptocurrency is no longer an unfamiliar concept. With the development of the digital economy, cryptocurrencies have gradually replaced some functions of traditional currencies. This research aims to measure and evaluate the impact of cryptocurrencies on financial markets by considering their effects on exchange rates, gold prices, oil prices, and stock indices. Data for the analysis were collected on a weekly basis from 1 January 2014 to 28 February 2021. The multiple linear regression model was used to examine the relationships in the research model using the statistical analysis software SPSS 22. The research results indicate that cryptocurrencies have an impact on the financial market. Specifically, the research also identified the inverse effect of currency pairs on cryptocurrencies and the interaction between different cryptocurrencies. Consequently, financial market regulators, especially the agency responsible for monitoring the volatility of cryptocurrencies, exchange rates, gold prices, oil prices, and stock indices, have a basis for devising appropriate plans. From this research, managers can implement policies that enhance financial education and communication to help individuals understand the nature of virtual assets, especially cryptocurrencies, while creating motivation towards accepting cryptocurrencies in Vietnam.

Kezban Hitay

Bu çalışmanın amacı, Bitcoin fiyatında meydana gelen değişimlerin bankaların finansal performansı üzerindeki etkisini belirlemektir. Bu doğrultuda BIST Banka endeksinde bulunan 10 bankanın, 2017-2022 yılları arasındaki çeyrek dönem verileri araştırma dönemi olarak belirlenmiştir. Finansal performansın tespiti için verilerin incelemesinde ve araştırılan etkinin tespitinde Panel Veri Regresyon analizinden yararlanılmıştır. Regresyon analizini uygulamadan önce değişkenler arasındaki ilişkiyi belirlemek amacıyla korelasyon analizi yapılmış ve değişkenler arasında negatif bir ilişkinin varlığı tespit edilmiştir. Ardından Panel regresyon analizi için iki model oluşturulmuştur. Modellerin analizi sonucunda ise her iki modelde de bağımsız değişken olan Bitcoin fiyatı ile bağımlı değişkenler arasında anlamlı bir ilişki tespit edilemezken, kontrol değişkeni ile finansal performansı ifade eden bağımlı değişkenler arasında negatif ve anlamlı bir ilişki olduğu sonucuna ulaşılmıştır.

Yi Zhang, Long Zhou, Yuxue Li, Fang Liu

This paper explores the relationships between the US dollar, crude oil, gold, and bitcoin by taking into account the higher-moment linkages. Specifically, we construct robust estimators for the realized volatility, realized skewness, realized kurtosis, and jump, and study the causalities between the estimators through the Granger causality test. A generalized impulse response analysis identified by our quad-variate VAR specification is further implemented to uncover the lead-lag spillover effect across the variables of interest. We utilize high-frequency data for the chosen assets from January 3, 2016, to June 23, 2022, and observe various patterns of cross-market interconnection related to higher-order moments. These findings suggest that systematic risk factors must be considered while jointly modeling market linkages. Practical implications for investors and market regulators are also discussed.

John W. Goodell, John W. Goodell, Miklesh Prasad Yadav, Junhu Ruan · 7 authors

This paper analyses the connectedness among traditional assets, digital assets and renewable energy for extending the data from December 31, 2019 to January 2, 2023. For an empirical analysis, time varying parameter (TVP-VAR) is employed. We find that Chainlink (DeFi) is the highest receiver, while bitcoin is the highest transmitter of shocks to the network. Additionally, we also find that Non-Fungible Tokens (NFT) acts as the most suitable asset to be included in portfolio since it is least connected with rest of the examined assets classes. Results are important for investors and portfolio managers.

İbrahim Yağlı, Özkan HAYKIR

The study aims to investigate the causality relationship between investor happiness and cryptocurrency returns. The study is focused on the five largest cryptocurrencies, specifically Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Ripple (XRP), and Cardano (ADA). Twitter-based Happiness Index is used to measure investor happiness. The sample period covers the period between January 1, 2019, and October 2, 2021. The Zivot-Andrews test is employed to detect stationary of covariates. After ensuring that all variables are stationary at levels, the Granger causality test is adopted to understand the relationship between the happiness index and cryptocurrency returns. The impulse-response functions are illustrated. The results indicate that there is a uni-directional relationship from BTC to Happiness Index, and Happiness Index to ETH. Considering that the causal relationship between cryptocurrency returns and investor happiness differs between cryptocurrencies, it is thought that investors should closely monitor the happiness index and make adjustments in their portfolios in response to changes in investor happiness.

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.

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.

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.

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.

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.

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.

Alexey Mikhaylov, Vikas Khare, Solomon Eghosa Uhunamure, Ts. Chang · 5 authors

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.

Sumaira Ashraf, António Almeida, Iram Naz, Rashid Latief

This study investigates the interconnectedness of the Islamic stock market, Bullion, and Bitcoin as diversifiers for portfolios, exploring their role as hedges and safe havens. The analysis covers the period from January 2015 to December 2022, with a particular focus on the influence of the COVID-19 pandemic and the Russia-Ukraine War on the MSCI World Islamic Index, bullions (Gold, Silver, Platinum, Nickel, Palladium, and Aluminium), and Bitcoin, employing a time-varying parameter vector autoregression (TVP-VAR) model. During crisis periods, our findings reveal that the transmission and reception of shocks among these assets varied, with a heightened level of co-movement observed during the pandemic and war periods. These results emphasise the importance of considering the dynamic nature of financial assets' connectedness in asset investment decisions, particularly in times of crisis. Furthermore, the findings suggest that Bullion can serve as a hedge for both Bitcoin and the Islamic stock market. The study also explores the optimal diversification of investment portfolios and highlights the importance of adhering to Islamic principles in portfolio diversification. By integrating Islamic rules into the diversification process, investors can enhance the effectiveness and relevance of their investment strategies.

Lana Swartz, Carola Westermeier

Central banks worldwide are developing, piloting and launching new central bank digital currencies (CBDCs). As the hub for the central banking community, the Bank for International Settlements (BIS) promotes a curiously botanical CBDC imaginary. From ‘money flowers’ to ‘tree trunks’ and a ‘strong canopy’, This helps to naturalize CBDC without clarifying its sociopolitical implications or envisioned monetary future, such as geopolitical tensions and financial fragmentation, new modes of financial interaction or the strengthened role of central banks. While omitting the paradoxes and ambivalences of CBDC, the imaginary of the BIS structures the enfolding discourses and allows the bank to function as a think tank for financial policy‐making.

Nidhal Mgadmi

<title>Abstract</title> In this paper, we try to examine the relationship between the Bitcoin price, social media metrics and the intensity of Covid-19 pandemic. We also attempt to investigate the behavior of Bitcoin volatility during such pandemic. For this end, we use the error correction model, Co-integration processing tool and vector error correction model to detect potential transmission mechanisms among different variables and the dynamic coupling between them. We also apply the GARCH-type models to better apprehend the behavior of Bitcoin volatility. Our results clearly display the short- and long term evidences of the relationshipbetween the Bitcoin price, severity of the Covid-19 health crisis and social media metrics. Moreover, there is strong evidence related to the information content of social media during turbulent phases. We also report some distinctive and salient features of Bitcoin volatility. The information spillover from pandemic-related news to the Bitcoin prices is well-documented. Using the Covid-19 deaths and confirmed cases can be considered as measure of pandemic severity. As well, the information transmission mechanism is well-documented through social media which seems to have an added value during the stressful periods. Such analysis could have insightful implications for investors in crypto-currency market.

Markus Frohmann, Manuel Karner, Said Khudoyan, Robert F. Wagner · 5 authors

Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the future price of Bitcoin, which is currently the most popular cryptocurrency. More precisely, we propose a hybrid approach, combining time series forecasting and sentiment prediction from microblogs, to predict the intraday price of Bitcoin. Moreover, in addition to standard sentiment analysis methods, we are the first to employ a fine-tuned BERT model for this task. We also introduce a novel weighting scheme in which the weight of the sentiment of each tweet depends on the number of its creator’s followers. For evaluation, we consider periods with strongly varying ranges of Bitcoin prices. This enables us to assess the models w.r.t. robustness and generalization to varied market conditions. Our experiments demonstrate that BERT-based sentiment analysis and the proposed weighting scheme improve upon previous methods. Specifically, our hybrid models that use linear regression as the underlying forecasting algorithm perform best in terms of the mean absolute error (MAE of 2.67) and root mean squared error (RMSE of 3.28). However, more complicated models, particularly long short-term memory networks and temporal convolutional networks, tend to have generalization and overfitting issues, resulting in considerably higher MAE and RMSE scores.

Haoyang Yu, Yutong Sun, Yulin Liu, Luyao Zhang

Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including unspent transaction outputs (UTXO), spent transaction outputs (STXO), Weighted Average Lifespan (WAL), CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.

Haoyang Yu, Yutong Sun, Yulin Liu, Luyao Zhang

Historically, gold and silver have played distinct roles in tra- ditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has com- monly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Lite- coin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including unspent transac- tion outputs (UTXO), spent transaction outputs (STXO), Weighted Average Lifespan (WAL), CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we’ve devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing re- sults not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee’s metaphor, underscoring Bitcoin’s superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we’ve made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.

Mortaza Ojaghlou, Özge DEMİRKALE

Türkiye, Brezilya, Hindistan, Güney Afrika ve Endonezya'nın ekonomik büyümelerini finanse etmek için istikrarsız yabancı yatırımlara olan yüksek bağımlılıkları nedeniyle, bu ülkeler “Kırılgan Beşli” ülke olarak adlandırılmıştır. Aynı zamanda Global Crypto Adoption Index'e göre, bu ülkeler kripto para birimlerine yatırım yapma konusunda oldukça aktiflerdir. Bu çalışmada “Kırılgan Beşli” ülkeler dikkate alınarak Bitcoin ve finansal varlıklar arasındaki uzun dönemli asimetrik ilişki Ağustos 2010 - Temmuz 2022 dönemine ait aylık veriler baz alınarak ARDL ve NARDL yöntemleri ile incelenmiştir. Pozitif ve negatif Bitcoin şoklarından kaynaklanan dinamik çarpanların doğrusal kombinasyonu, beş ülkenin tümü için NARDL üzerinden Dinamik çarpan testine başvurarak grafikleri çizilmiştir. Sonuçlar, Bitcoin'in tüm borsa endekslerine olumlu bir etkisi olmasına rağmen, yalnızca Türkiye ve Hindistan'daki değişkenlerin eş bütünleşik olduğunu göstermektedir. Bitcoin'in olumsuz şoklarının Türkiye'de daha derin ve baskın etkiye sahip olduğu anlaşılmıştır. Ancak, Bitcoin’in olumlu şoklarının Hindistan'da daha baskın olduğu sonucuna rastlanmıştır.