Blockchain Papers

Justin Banon, Jason Potts

No abstract is available for this record.

Zhuanxin Ding, Karamvir Gosal, Greg M. Mcmurran

No abstract is available for this record.

Krzysztof Gogol, Robin Fritsch, Malte Schlosser, Johnnatan Messias · 6 authors

This paper studies liquid staking tokens (LSTs) on automated market makers (AMMs), both theoretically and empirically. LSTs are tokenized representations of staked assets on proof-of-stake blockchains. First, we model LST-liquidity on AMMs theoretically, categorizing suitable AMM types for LST liquidity and deriving formulas for the necessary returns from trading fees to adequately compensate liquidity providers under the particular price trajectories of LSTs. For the latter, two relevant metrics are considered: (1) losses compared to holding the liquidity outside the AMM (loss-versus-holding, or "impermanent loss"), and (2) the relative profitability compared to fully staking the capital (loss-versus-staking) which is specifically tailored to the case of LST-liquidity. Next, we empirically measure these metrics for Ethereum LSTs across the most relevant AMM pools. We find that, while trading fees often compensate for impermanent loss, fully staking is more profitable for many pools, raising questions about the sustainability of the current LST liquidity allocation to AMMs.

Galin Georgiev

We propose a "break-even" implied volatility of a decentralized finance (defi) pool. The implied volatility is "break-even" because it is defined by the zero expected profit-and-loss of hedged liquidity providers i.e. by their expected profit (against the "buy-and-hold" benchmark) equaling their expected loss (against the same benchmark): the numerator of Rebalancing Loss a.k.a. Impermanent Loss. It depends only on the time-to-maturity and therefore forms only a curve (as opposed to the traditional surface). Similarly to traditional finance, when implied volatility is higher than realized volatility, option sellers (liquidity providers) are more likely to make money, irrespective of hedging. When approximated, this first-principles definition of implied volatility can be surprisingly (loosely) derived from the square-root market impact empirical rule in traditional finance.

Augustin Valéry

No abstract is available for this record.

Bin Liu, Tina Prodromou, Sandy Suardi, Caihong Xu

No abstract is available for this record.

Gunel Jannataly Amrahova

Globalization and irrational capital distribution have fuelled global financial crises. Illicit transactions on a global scale worsen financial challenges, limiting government spending on public services. Amid these challenges, blockchain technology and cryptocurrencies have emerged as potential solutions. Due to anonymity public identification through “keys”, some scholars argue that cryptocurrencies create an opportunity for misuse as an easy tool for money laundering, tax evasion and illegal activities. This study investigates the relationship between illicit finance flows and cryptocurrency markets, utilizing grey systems theory and grey relational analysis. Drawing samples from 41 states with the highest cryptocurrency trade volumes, the research reveals nuanced dynamics within the cryptocurrency market, shedding light on the connections between cryptocurrencies, shadow activities, and capital outflows. The findings contribute valuable insights to the ongoing discourse on the impact of cryptocurrencies on global financial stability. The intricate exploration of these interconnections underscores the need for a comprehensive understanding of the role cryptocurrencies play in shaping the contemporary financial landscape.

Shun Liu, Kexin Wu, Chufeng Jiang, Bin Huang · 5 authors

In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.

Grande, Mar, F. Borondo, J. Borondo

Currently cryptocurrencies and Decentralized Finance (DeFi), which enable financial services on public blockchains, represents a new growing trend in finance. In contrast to financial markets, ruled by traditional corporations, DeFi is completely transparent as it keeps records of all transactions that occur in the network and makes them publicly available. The availability of the data represents an opportunity to analyze and understand the market from the complexity that emerges from the interactions of the actors (users, bots and companies) operating in the embedded market. In this paper we focus on the Ethereum network and our main goal is to show that the properties of the underlying transaction network provide further and useful information to forecast the evolution of the market. We aim to separate the non redundant effects of the blockchain transaction network properties from classic technical indicators and social media trends in the future price of Ethereum. To this end, we build two machine learning models to predict the future trend of the market. The first one serves as a base model and considers a set of the most relevant features according to the current scientific literature including technical indicators and social media trends. The second model considers the features of the base model, together with the network properties computed from the transaction networks. We found that the full model outperforms the base model and can anticipate 46 more rises in the price than the base model and 19 more falls.

Ali Mehrban, Pegah Ahadian

This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and Ethereum Dominance (ETH.D) in a daily timeframe. The methods used to teach the data are hybrid and backpropagation algorithms, as well as grid partition, subtractive clustering, and Fuzzy C-means clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed in this paper has been compared with different inputs and neural network models in terms of statistical evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time.

Mustafa Kamal, Sabir Ali Siddiqui, Nayabuddin, Afaf Alrashidi · 11 authors

The study and investigation of the behavior of monetary phenomena is an interesting subject for actuaries and practitioners. In the recent age and development in the monetary and financial phenomena, cryptocurrency has gained much attention from actuaries. Over the past decade, several research studies have emerged on modeling and forecasting cryptocurrency exchange rates. This paper also contributes to the modeling of cryptocurrency exchange rates using a new version of the Logistic distribution, namely, a new cotangent-Logistic distribution. The mathematical properties and estimators of the new cotangent-logistic distribution's parameters are obtained. We illustrate the new cotangent-Logistic distribution using two financial data sets representing the log-returns of the Bitcoin and Ethereum prices. We compare the new cotangent-Logistic distribution with the baseline Logistic distribution and its modified version. Using the p-value and three other statistical tests, we show that the new cotangent-Logistic distribution repeatedly provides the optimal fit to cryptocurrency exchange rates.

Weichenyang Zhou

This thesis aims to understand and analyze the impact of China's virtual currency policy on the Bitcoin market. This paper will summarize the impact of digital technology on cryptocurrencies, including the development path, technological development, process, and regulation through literature search, data analysis, and government policies to comprehensively analyze the impact of the virtual currency policy on cryptocurrency circulation. Regulators and governments must formulate appropriate policies and measures to ensure compliance and stability in the cryptocurrency market. This paper analyzes the Bitcoin market as an example by using a mathematical model to reflect the trend and direction of the market before and after the release of the policy and using the model to predict future changes in the Bitcoin market. The Chinese government's regulatory measures have led to the closure of several Bitcoin miners, which has affected Bitcoin mining activity. Second, the decline in liquidity in the Chinese market has led to short-term volatility in the price of bitcoin. In addition, some investors may view Bitcoin as a safe-haven asset against an uncertain policy environment. Overall, China's virtual currency policies have had a multifaceted impact on the Bitcoin market, and these impacts are still evolving to some extent. This research provides valuable insights into the dynamics of the global Bitcoin market and the impact of virtual currency regulation.

Emin Karataş, Ayyüce Memiş Karataş

This research discusses the causal relationship among the exchange rates, 10-year bond yields, and Central Bank policy rates with regard to the countries known as the Fragile Five (F5) by comparing them to global indicators such as gold, Bitcoin price, and the Volatility Index (VIX). The study takes into consideration the bond yields, exchange rates, and interest rates of Türkiye, India, Indonesia, South Africa and Brazil in terms of their causal relationship with one another. The study also identifies some causal relationships among gold, bitcoin, and VIX with each other as global indicators by using the Toda Yamamoto approach to the Granger causality test. This study has arrived at the conclusion that a causal relationship exists between exchange rates and interest rates for Türkiye, Indonesia, and South Africa but not for Brazil or India. VIX is the most significant variable, as it is affected by seven different variables, including policy rates and different exchange rates. In addition, none of the variables are seen to Granger cause bitcoin’s price.

Zhouyun Zhao

The study explores the spillover effect on Ethereum – one of the leading cryptocurrencies – stemming from key variables in the domains of cryptocurrencies, investor sentiment, and traditional financial markets. This paper is the first to analyze the influence of such dominant representatives from diverse, external fields on cryptocurrency. We select bitcoin, the Fear and Greed index, the Standard and Poor’s 500 index and the United States Dollar to Euro Exchange Rate as representatives to investigate the spillover effect on Ethereum. Utilizing linear regression models and vector autoregressive (VAR) models, we find strong correlations between Ethereum’s return and that of Bitcoin’s, along with investor sentiment. However, the influence of financial market variables on Ethereum are found to be virtually static and negligible. This research offers valuable insights to those seeking to forecast or manipulate crypto market movement through analyzing the complex interplay between these variables and Ethereum.

Andrea Carotti, Cosimo Sguanci, Anastasios Sidiropoulos

The Bitcoin Lightning Network (LN) is designed to improve the scalability of blockchain systems by using off-chain payment paths to settle transactions in a faster, cheaper, and more private manner. This work aims to empirically study LN's fee revenue for network participants. Under realistic assumptions on payment amounts, routing algorithms and traffic distribution, we analyze the economic returns of the network's largest routing nodes which currently hold the network together, and assess whether the centralizing tendency is incentive-compatible from an economic viewpoint. Moreover, since recent literature has proved that participation is economically irrational for the majority of large nodes, we evaluate the long-term impact on the network topology when participants start behaving rationally.

Andromahi Kufo, Ardit Gjeçi, Artemisa Pilkati

The blossoming of cryptocurrencies during the last decade has largely influenced both the financial and the technological world. Bitcoin emerged on the edge of the financial crisis in 2008, signaling the very beginning of a financial and technological innovation, which in continuance would eventually create a lot of questions and debate previously unforeseeable. This paper aims to explore the impact of factors such as trading volume, information demand, stock returns, and exchange rates on the volatility of returns for decentralized and unbacked cryptocurrencies from 2016 to 2022 by employing the GARCH model. Based on each coin’s innate functional characteristics and market performance quantified by their respective market capitalization, the selection included Bitcoin, Ether, and XRP as representative crypto coins for the category of decentralized and unbacked cryptocurrencies. The implementation of correlation analysis and the use of the GARCH model on influencing factors for each coin revealed that decentralized and unbacked cryptocurrencies are positively related to trading volume, information demand, and exchange rates while being indifferent to a certain extent to the stock market returns of the world stock index MSCI ACWI. The results of this study provide further insight into the behavior of cryptocurrency return volatility in the new, ever-changing, and highly unpredictable crypto market as well as aid investors in their decision-making process concerning portfolio optimization.

Yensen Ni, Pinhui Chiang, Min-Yuh Day, Yuhsin Chen

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization.

Izz Eddien N. Ananzeh, Mohammad O. Al-Smadi

The purpose of this study is to examine the market efficiency of cryptocurrencies, specifically at a weak level. The study focuses on six prominent cryptocurrencies selected based on their significant market capitalization: Bitcoin (BTC), Tether (USDT), Ethereum (ETH), Binance Coin (BNB-USD), Ripple (XRP-USD), and Cardano USD (ADA-USD). The analysis utilizes unit root, Ljung–Box, variance ratio, runs, and the Brock–Dechert–Scheinkman (BDS) tests to assess different aspects of market efficiency. The data spans from September 2017 to April 2023, encompassing a wide time frame to capture potential shifts in market behavior. The results of all the tests, except the BDS test, indicate that the tested cryptocurrencies' markets are inefficient. However, the BDS test yielded different results, suggesting that BTC and ETH exhibit market efficiency compared to the other cryptocurrencies. This discrepancy indicates that the BDS test may be capturing different aspects of the time series behavior. The practical implication is that investors and market participants should exercise caution and consider the varying levels of efficiency when making decisions regarding these cryptocurrencies. Also, investors should consider a range of factors, including technical and fundamental analyses, when making investment decisions in a dynamic and evolving market.

Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali

This study finds breaks, trend breaks, and outliers in the last decade returns of five cryptocurrencies Bitcoin, Ethereum, Litecoin, Tether USD, and Ripple that experienced frequent changes. The study uses the indicator saturation (IS) approach to simultaneously identify breaks, trend breaks, and outliers in these returns to gain a deeper understanding in their dynamics. The study found that monthly, weekly and daily breaks existed in these returns as well as trend breaks, and outliers mostly during the market peaks in 2017, 2018, 2020, and 2021 that can be attributed to a number of things, such as the global Covid-19 pandemic in 2020, the 2021 crypto crackdown in China, the 2020 price halving of Bitcoin, and the 2017–2018 initial coin offering (ICO) boom. These returns also have common break segments and outliers. The application of IS technique to cryptocurrencies and simultaneous detection of market breaks, trend breaks, and outliers makes this study unique. This study is limited to considering only returns of five digital coins. These results may help traders, investors, and financial analysts modify their tactics and risk-management techniques to deal with the complexity of the cryptocurrency market.

Julien Chevallier, Bilel Sanhaji

In this paper, we conducted an empirical investigation of the realized volatility of cryptocurrencies using an econometric approach. This work’s two main characteristics are: (i) the realized volatility to be forecast filters jumps, and (ii) the benefit of using various historical/implied volatility indices from brokers as exogenous variables was explicitly considered. We feature a jump-robust extension of the REGARCH-MIDAS-X model incorporating realized beta GARCH processes and MIDAS filters with monthly, daily, and hourly components. First, we estimated six jump-robust estimators of realized volatility for Bitcoin and Ethereum that were retained as the dependent variable. Second, we inserted ten Bitcoin and Ethereum volatility indices gathered from various exchanges as an exogenous variable, each at a time. Third, we explored their forecasting ability based on the MSE and QLIKE statistics. Our sample spanned the period from May 2018 to January 2023. The main result featured the best predictors among the volatility indices for Bitcoin and Ethereum derived from 30-day implied volatility. The significance of the findings could mostly be attributable to the ability of our new model to incorporate financial and technological variables directly into the specification of the Bitcoin and Ethereum volatility dynamics.

Hilmi Tunahan AKKUŞ

Bu çalışmada altın ile kripto paralar arasındaki ilişkiler doğrusal olmayan modeller ile kapsamlı olarak araştırılmaktadır. Kripto paraları temsilen dijital altın olarak da adlandırılan en büyük kripto para Bitcoin ve en büyük akıllı kontrat platformu Ethereum çalışmada birlikte ele alınmaktadır. Hepsağ (2021) doğrusal olmayan eşbütünleşme testi bulgularına göre, ilgili değişkenler arasında çok zayıf düzeyde uzun dönemli ilişki, doğrusal olmayan Granger nedensellik testi sonuçlarına göre ise iki yönlü nedensellik ilişkisi tespit edilmiştir. Son olarak düzeltilmiş dinamik koşullu korelasyon (cDCC-GARCH) sonuçlarına göre altın ve kripto paralar arasında genellikle pozitif ve sıfıra yakın korelasyon bulunduğu, ancak COVID-19 salgınının görüldüğü 2020 yılı boyunca değişkenler arasındaki korelasyon ilişkisinin daha da arttığı belirlenmiştir. Elde edilen bulgular yatırımcılar için portföy çeşitlendirmesi, risk yönetimi ve piyasa öngörüsü açısından önemli bilgiler sunmaktadır.

R. Queiroz, Sérgio Adriani David

Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the volatility of five prominent cryptocurrency assets. Our analyses have been performed in both in-sample and out-of-sample cases. The results indicate that while distinct GARCH models can produce satisfactory in-sample fits, the Realized-GARCH model outperforms its counterparts in out of-sample forecasting. This paper contributes to the existing literature, since it better reveals the predictability performance of Realized-GARCH model when compared to other GARCH-types analyzed when an out-of-sample case is considered.

Pavlos I. Zitis, Shinji Kakinaka, Ken Umeno, Stavros G. Stavrinides · 6 authors

The COVID-19 pandemic has had an unprecedented impact on the global economy and financial markets. In this article, we explore the impact of the pandemic on the weak-form efficiency of the cryptocurrency and forex markets by conducting a comprehensive comparative analysis of the two markets. To estimate the weak-form of market efficiency, we utilize the asymmetric market deficiency measure (MDM) derived using the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach, along with fuzzy entropy, Tsallis entropy, and Fisher information. Initially, we analyze the temporal evolution of these four measures using overlapping sliding windows. Subsequently, we assess both the mean value and variance of the distribution for each measure and currency in two distinct time periods: before and during the pandemic. Our findings reveal distinct shifts in efficiency before and during the COVID-19 pandemic. Specifically, there was a clear increase in the weak-form inefficiency of traditional currencies during the pandemic. Among cryptocurrencies, BTC stands out for its behavior, which resembles that of traditional currencies. Moreover, our results underscore the significant impact of COVID-19 on weak-form market efficiency during both upward and downward market movements. These findings could be useful for investors, portfolio managers, and policy makers.

Nir Chemaya, Lin William Cong, Emma Jorgensen, Dingyue Liu · 5 authors

Decentralized Finance (DeFi) is reshaping traditional finance by enabling direct transactions without intermediaries, creating a rich source of open financial data. Layer 2 (L2) solutions are emerging to enhance the scalability and efficiency of the DeFi ecosystem, surpassing Layer 1 (L1) systems. However, the impact of L2 solutions is still underexplored, mainly due to the lack of comprehensive transaction data indices for economic analysis. This study bridges that gap by analyzing over 50 million transactions from Uniswap, a major decentralized exchange, across both L1 and L2 networks. We created a set of daily indices from blockchain data on Ethereum, Optimism, Arbitrum, and Polygon, offering insights into DeFi adoption, scalability, decentralization, and wealth distribution. Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.