With the increasing adoption of the Proof of Stake (PoS) blockchain, it is timely to study the economy created by such blockchain. In this chapter, we will survey recent progress on the trading and wealth evolution in a cryptocurrency where the new coins are issued according to the PoS protocol. We first consider the wealth evolution in the PoS protocol assuming no trading, and focus on the problem of decentralisation. Next we consider each miner's trading incentive and strategy through the lens of optimal control, where the miner needs to trade off PoS mining and trading. Finally, we study the collective behavior of the miners in a PoS trading environment by a mean field model. We use both stochastic and analytic tools in our study. A list of open problems are also presented.
This paper presents a dynamic equilibrium model of Ethereum's macroeconomy. The model captures agents' decisions regarding ETH holdings, staking, and the use of blockspace on both the Ethereum mainnet and Layer 2 networks. The ETH supply evolves according to protocol rules. The model's long-run behavior is characterized analytically, and key properties of the staking share and the price of ETH are derived. The model is calibrated using market data on ETH prices and transaction fees. Alternative issuance curves are evaluated for their effectiveness in managing staking levels.
Tim Weingärtner, Fabian Fasser, Pedro Costa, Walter Farkas
Decentralized finance (DeFi) promises a revolution in financial accessibility, transparency, and automation. Yet, its very novelty exposes participants to a number of additional risks and challenges. This study aims to address the risks associated with DeFi, while also conducting a comparative analysis to those of classical/traditional finance (TradFi). After introducing DeFi and its defining characteristics, such as the use of smart contracts, blockchain technology, and decentralized governance, the paper outlines the principal risks associated with DeFi. Drawing insights from an extensive literature review of 200 recent articles, of which 50 were thoroughly analyzed, the study compares risks of DeFi and TradFi, categorizing these into systematic and unsystematic risks. Furthermore, we introduce the ‘risk wheel’, an innovative tool tailored to understand and navigate the subtleties of DeFi risks, finding potential applications in risk assessment, management, and even education. This paper’s primary objective is to provide a detailed and impartial examination of the risks associated with DeFi and their comparison to traditional finance in order to assist stakeholders in making informed decisions and mitigating possible losses.
This study examines the price dynamics of Bitcoin and Ethereum between 2013 and 2022 using two distinct approaches: financial market technical analysis and econometric analysis. Financial market technical analysis employs indicators such as the Relative Strength Index (RSI) and the Hull moving average, while econometric analysis involves the Hodrick-Prescott filter and an Autoregressive Distributed Lag (ARDL) model. The study shows that Bitcoin and Ethereum experienced supercycle years in 2013, 2017, and 2021. The Bitcoin cycle, which averages 3.5 years, was particularly emphasized. The impact of the Bitcoin halving is also noteworthy, especially in the formation of supercycle bubbles in 2021, which affected altcoins such as Ethereum. The implications of this extend to portfolio management advice. It is recommended to carefully evaluate portfolio diversification and adopt a proactive regulatory approach, especially during the Bitcoin halving period.
Based on the global economic downturn, the price of Bitcoin has recovered, and new investors are constantly pouring into the Bitcoin market. To ensure that new investors have a basic understanding of Bitcoin and avoid unnecessary losses, this article will analyze Bitcoin's Trading Mechanisms, Price Influencers, and Trading Recommendations The main research finds that when Bitcoin is used as a currency, commodity, risk asset, and digital gold, the price factors are quite different. For example, when it is used as a risk asset, its price is affected by capital flows, market sentiment, and policy regulation. The multiple nature of Bitcoin will have a greater impact on investors' judgments. Through research, it is found that Bitcoin is still the virtual currency with the largest volume, the largest transaction volume, and the brightest future development prospects. The diversity of its nature brings risks but also brings a variety advantages of to the single currency or other financial products.
In the five years after the launch of bitcoin futures, academics and investors' perceptions have shifted from the early view that they raise the risk of bitcoin to the present acceptance of their ability to serve as derivatives. This change indicates that bitcoin futures have the potential to improve. What influences the bitcoin market has received from bitcoin futures is investigated in this paper. The introduction of bitcoin futures has offered a feasible hedging strategy for bitcoin investors, enhanced the stability and information effectiveness of the bitcoin market, also eased the investment barrier for bitcoin, based on study and comparison of previous research on bitcoin futures. However, because bitcoin futures are the novel type of futures contract, the market is complicated, and investors prefer to trade unregulated futures, whereas regulated futures are traded in considerably lesser quantities due to position limitations. Exchanges that regulate bitcoin futures should consider changing their contract positions to allow more investors to engage in trading while obtaining regulatory protections in to enable the bitcoin futures market to develop more maturely in the future.
In recent decades, data analytics has become increasingly involved in people's daily lives. Machine learning, an important part of data analysis, has also been used in the financial sector. Contemporarily, the high volatility feature of cryptocurrencies has attracted lots of investors, which also brings lots of difficulty to predict and analyze. In fact, the price of cryptocurrencies can also be forecasted based on machine learning. This paper uses historical data of Bitcoin, Ethereum and Dogecoin as inputs to predict the future value based on the LSTM. LSTM model can learn the long-term dependencies in data. According to the analysis, mean absolute error calculate the average size of the error in a set of predictions, regardless of its direction. The results produced can roughly predict the future trends of these three cryptocurrencies. This paper combines the fields of machine learning and finance to predict the future value of cryptocurrencies. These results shed light on guiding further exploration of predicting cryptocurrency assets valuation based on LSTM model.
Bu çalışmada, kripto piyasasının önde gelen altı kripto para biriminin (Bitcoin, Stellar, Litecoin, Ethereum, Tether ve Ripple) volatil yapısı, asimetrik ilişki ve/ve ya kaldıraç etkisinin var olup olmadığı test edilmektedir. 09/11/2017-31/07/2022 dönemini kapsayan ve WinBUGS uygulaması ile yapılan bu çalışmada öncelikle logaritmik fark alınarak getiri serisi hesaplanmıştır. Bu kapsamda 100.000 tekrarla örneklem sınaması yapılmış olup katsayıların başlangıç eğiliminden çıkması için tahminlerin ilk 10.000 örneklemi dışlanarak kalan 90.000 örneklemle analiz gerçekleştirilmiştir. Asimetrik stokastik volatilite modeli tahmin sonuçlarına göre kripto para birimlerinin oynaklık kalıcılığı, oynaklığın öngörülebilirliği ve para birimlerinin kendi getirilerinin şoku ile oynaklıklarının etkisi arasındaki korelasyon düzeyi ilgili parametreler ile değerlendirilmiştir. Belirtilen zaman aralığında çalışmamızda kullanılan tüm kripto para birimleri için yoğun bir volatilite kümelenmesi olduğu gözlemlenmiştir. Bu volatilitenin sürekli olduğu ve düşük öngörülebilirliğin varlığı ampirik olarak asimetrik stokastik volatilite modeli ile elde edilen bulgular arasındadır. Ayrıca çalışmanın sonuçlarına göre Ethereum kripto para birimi dışındaki diğer beş para biriminin hiçbirinde ne kaldıraç etkisi ne de asimetrik ilişkisinin hiçbiri gözlemlenmemiştir.
In recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions. This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 – 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.
In this study we investigate possible long-range trends in the cryptocurrency market. We employed the Hurst exponent in a sample covering the period from 1 January 2016 to 26 March 2021. We calculated the Hurst exponent in three non-overlapping consecutive windows and in the whole sample. Using these windows, we assessed the dynamic evolution in the structure and long-range trend behavior of the cryptocurrency market and evaluated possible changes in their behavior towards an efficient market. The innovation of this research is that we employ the Hurst exponent to identify the long-range properties, a tool that is seldomly used in analysis of this market. Furthermore, the use of both the R/S and the DFA analysis and the use of non-overlapping windows enhance our research’s novelty. Finally, we estimated the Hurst exponent for a wide sample of cryptocurrencies that covered more than 80% of the entire market for the last six years. The empirical results reveal that the returns follow a random walk making it difficult to accurately forecast them.
Leonardo H.S. Fernandes, Werner Kristjanpoller, Benjamin Miranda Tabak
This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior [Formula: see text]. Moreover, COVID-19 promoted an increase in the multifractal spectrum’s width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.
Abstract: Cryptocurrency, also known as crypto, is any digital or virtual currency that uses cryptography to safeguard transactions and circulates without the authority of a central bank. Bitcoin, the first and most widely used decentralized cryptocurrency, was introduced in 2009. After a few years of unrivalled dominance, it lost its monopoly in 2011, when the first competitive alternative currencies arose. As of November 2022, there are almost 21,000 cryptocurrencies in circulation. Because there is no government credit backup, cryptocurrency prices are typically volatile. The cost of one Bitcoin rose from zero at its debut in 2009 to $13 in 2013 and then to $68789 in 2021, with numerous shifts and fluctuations along the way. The accurate forecasting of the Bitcoin price is critical for investors to make decisions and for governments to create regulatory laws. This paper examines the ability of the models - Prophet, Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost) to predict the price of Bitcoin reliably. Using the performance metrics like RMSE, each model was thoroughly trained and tested to discover which one operates more efficiently. After examining the price of Bitcoin from 2012 to 2021, we concluded that the Long Short-Term Memory (LSTM) model proves to be the most efficient when dealing with variable and difficult-topredict data such as Bitcoin values since it portrays promising results in comparison
Bitcoin as a decentralized cryptocurrency is the most popular cryptocurrency in the world today. Such that, a lot of people know it as a way to invest their money, which shows that is vital to predict the price trend of this cryptocurrency. In this article, we are trying to predict the price of Bitcoin using a machine learning approach. To do so, we apply collected data from trading sources to train an artificial deep neural nenvork model, and then use the model to predict Bitcoin fluctuation. Our deep model results show that it achieves the best performance among other methods.
Abstract Based on asset pricing theory, we posit and find that equity markets and cryptocurrency markets share a common fundamental. Our cointegration tests show that the most important asset pricing primitive, consumption, can serve as the common fundamental. We further show that additional macroeconomic factors, as well as uncertainty and sentiment, all play a role in explaining the deviation from fundamentals. To understand the linkage between equity markets, cryptocurrency markets, and the macroeconomy, we suggest the following three channels: (i) portfolio allocation decisions, (ii) intermarket order flows, and (iii) technological adaption expectations.
We develop a new privacy-preserving framework for a general class of financial network models, leveraging cryptographic principles from secure multiparty computation and decentralized systems. We show how aggregate-level network statistics required for stability assessment and stress testing can be derived from real data without any individual node revealing its private information to any outside party, be it other nodes in the network, or even a central agent. Our work bridges the gap between established theories of financial network contagion and systemic risk that assume agents have full network information and the real world where information sharing is hindered by privacy and security concerns. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4582 .
Purpose The mining process is essential in cryptocurrency networks. However, it consumes considerable electrical energy, which is undoubtedly harmful to the environment. In response, energy-conserving cryptocurrency projects with reduced energy requirements or based on renewable energies have been developed. Recently, the COVID-19 pandemic and the Russian invasion of Ukraine ignited an unprecedented upheaval in financial products, especially in cryptocurrency and energy markets. Therefore, the paper aims to explore the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict. Design/methodology/approach This paper investigates the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict. Their competitiveness is compared with conventional ones by analyzing their efficiency through multifractal detrended fluctuation analysis and automatic variance ratio during the COVID-19 and Russian invasion periods. Findings The empirical results show that all investigated energy-conserving cryptocurrencies negatively responded to the pandemic and positively reacted to the Russian invasion. On the other hand, all conventional cryptocurrencies reacted negatively to the COVID-19 pandemic and the amid-Russian attack. Besides, Bitcoin and SolarCoin were the least inefficient before the outbreak of COVID-19. Nevertheless, the Ethereum market became the most efficient after the pandemic spread. Similarly, the efficiency of Ripple was the most significant during the conflict between Russia and Ukraine. The energy crisis caused by Russia benefited the efficiency of the studied energy-conserving cryptocurrencies. Practical implications This research is of interest to investors seeking opportunities in these energy-conserving cryptocurrencies and policymakers working to implement reforms to improve their market efficiency and promote long-term financial market growth. Originality/value To the best of the authors' knowledge, the behavior of cryptocurrencies based on renewable and reduced energy during the recent conflict between Russia and Ukraine has not been explored.
With a market cap of $1.9 trillion and more than 10 thousands active trading cryptocurrencies, the global crypto market is claimed to be an attractive and vibrant market that strongly attracts many participants. Studying cryptocurrency price tendency is one of the most challenging and interesting research fields. Despite the increasing number of studies tackling this field, it is essential to understand the factors influencing the price and analyze the most efficient model for working with crypto data. This study applies three regression models: Multiple Linear Regression, Ridge Regression, and Lasso Regression for cryptocurrency price prediction. By exploiting features that are directly tied to the closing price, the study evaluates the performance of three models on four distinct cryptocurrencies. The final results reveal the outstanding performance of Lasso Regression that could be applied in the crypto context for future studies.
Many investors include cryptocurrencies as potential investment tools in their portfolios. Previous studies have mostly analyzed Bitcoin regarding its hedge and safe haven features. Although the cryptocurrency market has expanded far beyond Bitcoin, few studies have examined the interaction among all other cryptocurrencies and conventional financial assets. For this purpose, as the dependent variable, we included the cryptocurrency index to represent the cryptocurrency market, whereas international stocks, bonds, United States (US) dollars, gold, and commodities as independent variables in the analysis. The interactions among the variables were analyzed using the Granger causality tests. The analysis results revealed a two-way causality relationship between the cryptocurrency market and the bond markets, indicating that the cryptocurrency index can be used to predict bond prices and vice versa.
Bitcoin prices are highly volatile and have extreme upper tails of the return distributions. One important component of Bitcoin price jumps is that it does not follow a normal distribution. This present study aims to reduce the extreme value data available on Bitcoin into simple clusters based on extreme value returns. The study first measures the excessive volatility and then estimates the extreme value returns of Bitcoin between November 2013 and August 2022 to achieve this objective. For robustness checks, extreme value returns are estimated using both the Rogers and Satchell (RS) and the Variance Ratio (VRatio) estimators that embed jumps in the model. Further, K-means clustering is used to form clusters based on the estimated Bitcoin’s extreme value returns as the probable good days (extreme days), medium days, and bad days. The study observes that K-means clustering can explain 65 percent point return variability. The study findings will be highly useful for crypto investors, policymakers, and future studies in data mining.