This paper examines and compares intraday and intraweek patterns in hourly and daily prices, returns, volumes and volatility of native cryptocurrencies, stablecoins and tokens traded on Bitstamp. We show that native cryptocurrencies and tokens share common intraday periodicity determined by the operating times of the NYSE, LSE and Hang Seng stock exchange markets. Periodic patterns are also documented in the returns on cryptocurrency market portfolio approximated by the PCA applied to intraday and intraweek cross-sectional correlation matrices of cryptocurrency returns. Stablecoins have distinct dynamics and their daily and hourly returns are uncorrelated with one another and with the returns on other cryptocurrencies. We introduce a functional CAPM to accommodate the periodic patterns and estimate it by regressing the functions of intraday and intraweek cryptocurrency returns on the market portfolio. We show that the return functions on Bitcoin, Ether, and Link satisfy affine relationships with the return functions of the market portfolio and their functional betas display periodic intraday and intraweek patterns. • Native cryptocurrency and tokens share common periodic patterns. • Stablecoins have distinct intraday and intraweek dynamics. • The returns on stablecoins are uncorrelated with other cryptocurrencies. • Tokens contribute more to the risk on cryptocurrency market than other coins. • The betas in functional CAPM of cryptocurrency are periodic functions.
Kin-Hon Ho, Yun Hou, Michael Georgiades, Ken C. K. Fong
The emerging cryptocurrency market is one of the largest financial markets in the world, with a market capitalization that is already surpassing the gross domestic product of many developed economies. Cryptocurrencies are increasingly being adopted as a means of transaction and ownership in the digital domain, particularly in areas like decentralized finance and non-fungible tokens. Known for its high volatility, this market offers investors the potential for higher returns than traditional financial markets like stocks, foreign exchange, and commodities. However, it remains underexplored in academic research. In this paper, we propose the use of social network analysis to effectively model and analyze the cryptocurrency market and conduct a comprehensive numerical study to explore its key properties, including correlation structure, topological characteristics, stability, and influence. Furthermore, we propose the use of centrality measures as novel indicators to improve the accuracy of cryptocurrency price movement predictions. Our research introduces a novel method for understanding and navigating the cryptocurrency market, enabling investors to integrate advanced analytical tools into their decision-making processes.
This study examines the connectedness between technology stocks, cryptocurrencies, and non-fungible tokens (NFTs) using daily returns and risk data. We found that while there is strong connectedness within asset classes, connectedness between different types of assets is weak. Structural breaks in the VAR system did not change the degree of connectedness. Our findings suggest that interconnectivity between these assets is not significant enough to indicate a high level of correlation. This research provides valuable insights into the interplay between these markets and suggests diversifying portfolios to mitigate risks associated with these assets.
Mohamed Fakhfekh, Azza Béjaoui, Aurelio F. Bariviera, Ahmed Jeribi
This paper investigates the connectedness among eighteen cryptocurrency assets including NFT, DeFi, gold-backed cryptocurrencies, and traditional cryptocurrencies. We also compute the Optimal hedge ratio for each pair of (gold-backed) cryptocurrency-NFT/DeFi and assess their hedge effectiveness. To this end, we use a combination of econometric methods. Our sample period goes from 01/11/2021 to 21/02/2023, making the empirical analysis insightful and interesting as it includes the Covid-19 health crisis and the Russia–Ukraine war. Our empirical findings highlight the dissimilarities between different cryptocurrencies in terms of connectedness with NFT/DeFi assets. They also reflect the diversification benefits generated by the inclusion of gold-backed cryptocurrencies into NFT/DeFi portfolios, in particular in times of unprecedented events. These findings could be useful for crypto-investors who search to diversify their portfolios.
Cryptocurrency is starting to be considered as an asset class for investment portfolios because of the multiple competitive advantages it has and its beneficial correlation to other asset classes. Most investors in cryptocurrency are speculators driven by market sentiment, investing according to technical analysis. There is a gap between technical analysis and fundamental analysis in the area of cryptocurrency. With the adoption of fundamental analysis the real intrinsic value of cryptocurrency can be achieved with higher returns being gained. This research aims to identify key variables and valuation metrics of Ethereum Blockchain Networks in order to predict the intrinsic value of ether through linear multiple regression. This will involve presenting a model including fundamental variables of the Ethereum Blockchain Network and market sentiment with the objective of achieving higher returns for investors of ether. There will be a focus on fundamental analysis, rather than technical analysis, of cryptocurrency because it is presume that has a greater relation to the intrinsic value of cryptocurrency. Based on the research's unsupervised method of linear regression, a price prediction model of ether with a Mean Sum Square Error of 1.1266*e^-6 and R square of 99% is devised. The results indicate that the features of the Ethereum Blockchain Network and valuation metrics have more predicting power than the market sentiment (Crix-Crypto Index). The research highlight that the most significant variable to ether are gas price per block, transactions fees and reward to miners, and focused on the utility of ether which can be of intrinsic value and have a significant impact on investment portfolios.
This study examines Bitcoin price movements from an infectious disease perspective. The author compares the outbreak of the COVID-19 pandemic with the Bitcoin price explosion and adopts the SIR epidemiological model. The SIR model operates by categorizing the population of individuals into susceptible (S), infected (I), and removed (R). In the case of Bitcoin, open wallets represent the susceptible population, and the infection starts with a single individual. After conducting four estimation trials, the model that uses the recovery rate derived from the Bitcoin price downtrend and the infection rate from the upward trend has the highest accuracy. The estimation deviates from the Bitcoin price explosions by only three days. Previous studies commonly use faster-than-exponential growth or stationarity tests to identify bubble formations. This paper introduces a novel approach that employs epidemiological models to analyze Bitcoin's explosive price behavior.
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.
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.
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.
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.
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.
The temporal conduct of the cryptocurrency BIT GREEN Crypto is examined using an ARMA model. This study analyses BIT GREEN Crypto's volatility using the ARMA model. ARMA model examination of past pricing data determines BIT GREEN Crypto timing trends and variations. This study uses rigorous methods and historical data to reveal BIT GREEN Crypto's temporal patterns and changes to better cryptocurrency analysis. In the study, ARMA modelling correctly predicted BIT GREEN Crypto's volatility. The study helps investors and market participants understand cryptocurrency volatility. The results also show that the ARMA model's restrictions and the aspects of bitcoin volatility must be addressed. This study clarifies BIT GREEN Crypto's volatility and temporal dynamics. This ARMA-modelled study gives investors and market participants cryptocurrency insights and management advice.
Amit Kumar, Neha Sharma, Rahul Chauhan, Manish Sharma
Cryptocurrencies, most notably Bitcoin, have experienced a significant increase in popularity, garnering the interest of both investors and scholars. The present study aims to investigate and forecast the prices of Bitcoin. The focus lies on the essential aspects of data preprocessing, exploratory data analysis, and forecasting methodologies. The dataset undergoes thorough cleaning procedures to ensure meticulousness, followed by an exhaustive exploration of the data through various analytical techniques. This study provides valuable insights into pricing trends, seasonality patterns, and relationships within the dataset. The research utilizes a range of models, such as ARIMA for short-term prediction, LSTM neural networks for intricate pattern detection, and hybrid models for enhanced resilience. The evaluation of model performance is conducted by using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), while also employing out-ofsample testing to evaluate the model's ability to generalize. The results of this study provide a comprehensive analysis of the advantages and disadvantages associated with each technique, thereby offering significant insights for investors aiming to effectively navigate the cryptocurrency market. Furthermore, this highlights the possibility of applying this analysis to additional cryptocurrencies and improving forecasting models through the incorporation of sentiment analysis and macroeconomic indicators.
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.
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.
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.
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.