Blockchain Papers

Zehra Atik, Güven Murat, Bülent Güloğlu, Gülşah Hançerlioğulları Köksalmış · 5 authors

No abstract is available for this record.

David Alaminos, M. Belén Salas, Ángela Callejón Gil

<abstract> <p>The blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict "buy" or "sell" decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.</p> </abstract>

Krzysztof Koszewski, Somnath Mazumdar, Anoop Kumar

Abstract In recent years, cryptocurrencies have been considered as an asset by public investors and received much research attention. It is a volatile asset, thus predicting its prices is not easy due to the dependence on multiple external factors. Machine learning models are becoming popular for cryptocurrency price predictions, while also considering social media data. In this article, we analyze the rate of return of three cryptocurrencies (Bitcoin, Ether, Binance) from an investor point of view. We also consider three traditional external variables: S&P 500 stock market index, gold price, and volatility index. The rate of return prediction is based on three stages. First, we analyze the correlation between the cryptocurrency returns and the traditional external variables. Next, we focus on the influential social media variables (from Twitter, Reddit, and Wikipedia). Later, we use these variables to improve prediction accuracy. Third, we test how the standard time series models (such as ARIMA and SARIMA) and four machine learning models (such as RNN, LSTM, GRU and Bi-LSTM) predict one-day rate of return. Finally, we also analyze the risk of investing in each cryptocurrencies using value risk statistics. Overall, our result shows no correlation between cryptocurrency returns and three traditional external variables. Second, we found that overall LSTM model is the best, GRU is the second-best prediction model, while the impact of the social media variables varies depending on the cryptocurrencies. Finally, we also found that investment in gold offers better returns than cryptocurrency during Covid-19-like situations.

Qichuan Huang

This study investigates the Fear & Greed Index, an indicator designed to reflect market sentiment regarding Bitcoin price, intending to utilize it as a predictive parameter for future price fluctuations. Due to the substantial volatility in Bitcoin prices and its significant influence on prediction outcomes, the dataset was preprocessed through monthly filtering and normalization. To forecast Bitcoin prices, an array of machine learning algorithms, including linear regression, random forest, and XGBoost, as well as their enhanced counterparts, were employed. The optimal model was identified by comparing the Grid Search XGBoost analysis results. This research holds implications for accurately predicting Bitcoin prices and underscores the impact of market sentiment on its valuation.

Yongrong Huang, Huiqing Wang, Zhide Chen, Chen Feng · 7 authors

Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies-Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)-over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets.

Eugenio Cerutti, Jiaqian Chen, Martina Hengge

The rapid growth of crypto assets raises important questions about their cross-border usage. To gain a better understanding of cross-border Bitcoin flows, we use raw data covering both on-chain (on the Bitcoin blockchain) and off-chain (outside the Bitcoin blockchain) transactions globally. We provide a detailed description of available methodologies and datasets, and discuss the crucial assumptions behind the quantification of cross-border flows. We then present novel stylized facts about Bitcoin cross-border flows and study their global and domestic drivers. Bitcoin cross-border flows respond differently than capital flows to traditional drivers of capital flows, and differences appear between on-chain and off-chain Bitcoin cross-border flows. Off-chain cross-border flows seem correlated with incentives to avoid capital flow restrictions.

Panayiotis M. Pourpourides

We investigate the long-term impact of macroeconomic and financial factors on cryptocurrency metrics using both parametric and non-parametric methods. Our analysis examines how these factors influence cryptocurrency prices, market capitalizations, and Bitcoin’s hash rate. The results establish that two key factors, the US dollar and the price of gold, adversely affect Bitcoin and other cryptocurrency metrics, including the prices and market capitalizations of decentralized finance and layer-one protocols. Bitcoin’s hash rate demonstrates greater market sensitivity than its price, with the dollar having a stronger impact on Bitcoin than gold. The dollar primarily affects Bitcoin’s price, whereas gold mainly influences its hash rate. These findings, along with Bitcoin’s properties, support the view of Bitcoin as a digital asset analogous to physical gold, playing a role similar to a substitute for the latter.

Wei Wang, Haibo Wang

This study aims to examine the intricate dynamics between BRICS traditional stock assets and the evolving landscape of cryptocurrencies. Using a time-varying parameter vector autoregression model (TVP-VAR), we have analyzed data from the BRICS stock market index, cryptocurrencies, and indicators from January 6, 2015, to June 29, 2023. The results show that three out of the five BRICS stock markets serve as primary sources of shocks that subsequently affect the financial network. The transcontinental (TCI) value derived from the dynamic conditional connectedness using the TVP-VAR model demonstrates a higher explanatory power than the static connectedness observed using the standard VAR model. The discoveries from this study offer valuable insights for corporations, investors, and regulators concerning systematic risk and investment strategies.

Houda Ben Mabrouk, Imen Ben Khalifa

This paper examines the asymmetric spillovers between Bitcoin, oil and four precious metals (silver, gold, platinum and palladium) on daily returns from 18 August 2011 to 2 October 2019.Using a modified version of the Dieblod and Yilmaz (2012, 2014) index and a similar approach to Barunk (2017), our results indicate slight volatility spillovers between the whole systems.Moreover, the results show that gold is the most influential market since it shifts the highest proportion of volatility.Furthermore, we find that oil, Bitcoin and platinum can serve as a hedge and a diversifier as they are neutral in terms of spillovers.Moreover, we find evidence of asymmetric volatility spillovers since good spillovers dominate bad one, which proves the optimistic mood of the whole system.More interestingly, our results shed light on the ability of Bitcoin, the digital gold, to serve as a hedge and diversifier in both good and bad innovations.

Xiyong Dong, Zhuhua Jiang, Seong‐Min Yoon

No abstract is available for this record.

Wang Chun Wei, Dimitrios Koutmos, Min Zhu

No abstract is available for this record.

José Daniel Cardoso Rodrigues, Petros Golitsis, Pavlos Gkasis

With the rise of cryptocurrencies and their appeal as alternative investment assets, this study, using daily and weekly data from early 2015 to late 2023, aims to analyze the influence of economic and geopolitical uncertainty factors on cryptocurrencies, particularly Bitcoin, and forecast their volatility using GARCH, EGARCH, and GJR-GARCH models. Our findings reveal that the Geopolitical Acts Index (GPAs), the U.S. Economic Policy Uncertainty Index (EPU), and the Volume of Bitcoin transactions exhibit a positive significant impact on its returns, whereas the Cryptocurrency Uncertainty Index (UCRY), S&P 500, and Volatility Index (VIX) demonstrate a negative one. Furthermore, by decomposing geopolitical turbulence into Geopolitical Risks (GPRs) and Threats (GPTs), these variables were found to be less significant compared to Geopolitical Acts. Finally, the asymmetry analysis (leverage effects) reflects on how negative shocks exhibit a greater influence than positive ones on Bitcoin returns, indicating that adverse news in the media tends to impact the cryptocurrency returns more profoundly. Our conclusions contribute to the existing literature by exploring the role that Bitcoin, and cryptocurrencies in general, play as investment assets, when taking into consideration the volatility they entail, especially following negative shocks in an economy.

Lukas Mueller

No abstract is available for this record.

Ismail Jirou, Ikram Jebabli, Amine Lahiani

No abstract is available for this record.

Min-Bin Lin, Cathy Yi‐Hsuan Chen, Wolfgang Karl Härdle

This study investigates cryptocurrency volatility dynamics, particularly focusing on Ethereum (ETH). We dissect long- and short-term volatility components to gain deeper insights into its evolution. This approach allows studying the impact of ETH’s Merge upgrade, replacing Proof-of-Work with Proof-of-Stake on September 15, 2022. Employing 29 empirical factors related to blockchain functionality and crypto market characteristics, we explore their long-term equilibrium connection with price volatility. Our findings reveal that scalability factors and wealth dis- tribution significantly influence volatility persistence, ultimately highlighting the stability-enhancing impact of Ethereum’s Merge upgrade.

Myungwan Kim, Ye Jin Jeong, Jaehong Jeong

This study examines the impact of incorporating cryptocurrencies into global asset portfolios using ensemble approaches and a tracing strategy. We considered cryptocurrency ratios of 1%, 3%, and 5% for including cryptocurrencies. Benchmarking was performed using classical portfolio optimization strategies such as minimum variance portfolio (MVP), maximum diversification portfolio (MDP), equal risk contribution portfolio (ERCP), and hierarchical risk parity (HRP). The ensemble methods and tracing strategies we evaluated were the equally weighted portfolio (EWP), the linear combination portfolio (LCP), the return tracing portfolio (RTP), and the return volatility tracing portfolio (RVTP). EWP averages the weights of classical methods, while LCP combines the objective functions of three optimization methods. RTP and RVTP represent tracing strategy portfolios with monthly rebalancing, selecting the best-performing portfolio based on cumulative returns or a combination of cumulative returns and annualized volatility. Our findings reveal that increasing the cryptocurrency allocation improves performance metrics in ensemble portfolios but also leads to higher risk. In addition, including cryptocurrencies reduces transaction fees, especially evident in the LCP with a 5% allocation. In the case of a 3-month RTP, HRP emerged as the preferred strategy, outperforming the use of HRP alone. In the case of a 6-month RVTP, MVP remained the preferred choice, consistently achieving lower volatility.

Ilyas Agakishiev, Wolfgang Karl Härdle, Denis Becker, Xiaorui Zuo

Abstract There are many ways to model complex time series. The simplest approach is to increase the complexity, and thus, the flexibility of the model, for the entire time series. As an example, one could use a neural network. Another solution would be to change the parameters of a model dependent on the “state” or “regime” of the time series. A typical example here would be the Hidden Markov model (HMM). This paper combines the two concepts to create a Reinforcement Learning (RL) model that adds variables that depend on the state of the time series. To test the concept, the RL model is used with cryptocurrency data to determine the share to invest into the cryptocurrency index CRIX in order to maximize wealth. The results have shown that cryptocurrency metadata is useful as supplementary data for analysis of the respective prices. The Reinforcement learning model with regimes shows potential for investment management, but comes with some caveats.

Marina Estato Apsan Frediani

This article provides a comparative analysis of financial regulations across different jurisdictions, including the United States the UK and the European Union that could be applied to crypto market. It discusses the economic, social, and technological factors driving the need for crypto regulation and explores the challenges and opportunities these regulations present for financial stability, consumer protection, and innovation. By examining the different regulatory approaches, the article offers insights into the development of a balanced regulatory framework that addresses the unique aspects of digital currencies, maintaining the innovative approach of the crypto market while safeguarding against risks.

Harold Rodriguez, Jéfferson Augusto Colombo

Spot bitcoin ETFs have been recently approved in the U.S., increasing retail and insti tutional investors’ attention to crypto. To contribute to the debate on whether bitcoin protects against inflation, we analyze the effect of inflation shocks on bitcoin returns through the estimation and inference of Vector Autoregressive Models (VARs), iden tifying inflation shocks as surprises in the U.S.’s CPI and Core PCE announcements. Based on monthly data between August 2010 and January 2023, the results indicate that bitcoin returns increase significantly after a positive inflationary shock, corrob orating empirical evidence that bitcoin can act as an inflation hedge. However, we observe that bitcoin’s inflationary hedging property is sensitive to the price index – it only holds for CPI shocks – and to the period of analysis — the hedging property stems primarily from sample periods before the increasing institutional adoption of BTC (“early days”). Notably, the inflation hedge property of bitcoin (Gold) has dis appeared (strengthened) from the COVID-19 outbreak onwards. We conclude that the inflation-hedging property of bitcoin is context-specific and is likely to be diminishing as adoption increases.

Yüksel Akay Ünvan

Cryptocurrencies, which have begun to become an important rival to cash due to the changing lifestyle and technological developments, are gradually increasing their coverage area. Whether Bitcoin prices, which have exhibited different behaviors over the years since the day they were developed, are on a rational basis has become an important topic of discussion. Within the scope of this study, bitcoin prices between 2010 and 2023 were analyzed and factors that could make price behavior meaningful were tried to be determined. In addition, a forecast was also made in which Bitcoin prices for the coming years were calculated on a daily basis together with various statistical parameters using the the triple exponential smoothing method based on same historical data, and the results were discussed from various perspectives. In Bitcoin prices, which change mainly within the framework of supply and demand balance, attention has been drawn to the importance of different factors such as rational or irrational herd behavior, decisions taken about Bitcoin or news that may affect this balance and fall within the scope of behavioral finance. Along with the behavioral finance parameters that will make Bitcoin price behavior meaningful, it may not always be possible to attribute some changes in the relevant data to a specific reason. The main view supporting this situation is based on the personal nature of cryptocurrency itself.

An‐Sing Chen, Huong Thi Nguyen

No abstract is available for this record.

Mieszko Mazur, Efstathios Polyzos

Inflows to the newly-established bitcoin exchange traded funds (ETFs) surpassed $20 billion in the first several weeks of trading and are considered historic high by ETF standards. In this paper we provide early examination of the bitcoin spot ETFs listed on US exchanges, and their effect on bitcoin price formation. We establish several empirical facts: (1) daily capital flows to spot bitcoin ETFs exceed $500 million or roughly 10,000 bitcoins and surpass daily production of bitcoin by the factor of 5; (2) net flows to ETFs are a strong positive predictor of bitcoin price with R-squared of 95%; (3) most of bitcoin price appreciation is generated outside of the ETF trading hours; (4) increase in bitcoin price leads to an abnormal ETF trading volume; (5) inflows to bitcoin ETFs witness outflows from gold ETFs. Overall, during the period studied, capital flows to spot bitcoin ETFs emerge as a dominant single factor predicting positive valuation effects of bitcoin.

Adela Bârã, Irina Georgescu, Simona‐Vasilica Oprea, Marian Pompiliu Cristescu

In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec’s Probability of Failure (POF) test and Christoffersen’s test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSD→BRENT and BTCUSD→S&P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning.

Kuo‐Shing Chen

<abstract> <p>In this article, we describe the novel properties of Bitcoin and green financial assets and empirically examine the connectedness between Bitcoin and two green financial assets (i.e., carbon emissions, green bonds) and two representative markets of conventional assets (i.e., oil and emerging stock). This study also analyzes whether Bitcoin, carbon, green bonds, oil, and emerging stock assets can hedge against any market turbulence. From observed findings, Bitcoin was not an effective substitute for green bond assets. Thus, Bitcoin is not a valuable hedge instrument to substitute green bonds to mitigate climate risks. More precisely, the findings of the study show that carbon assets outperform emerging stock assets amidst the COVID-19 crisis, while the stock markets incurred significant losses. Crucially, the innovative findings also played an important role for policymakers interested in decarbonizing the crypto-assets.</p> </abstract>