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3,636 results · page 56 of 152
Ata Assaf, Khaled Mokni, Imran Yousaf, Avishek Bhandari
No abstract is available for this record.
Dev Churiwala, Bhaskar Krishnamachari
Automated Market Makers (AMMs) have cemented themselves as an integral part of the decentralized finance (DeFi) space. AMMs are a type of exchange that allows users to trade assets without the need for a centralized exchange. They form the foundation for numerous decentralized exchanges (DEXs), which help facilitate the quick and efficient exchange of on-chain tokens. All present-day popular DEXs are static protocols, with fixed parameters controlling the fee and the curvature - they suffer from invariance and cannot adapt to quickly changing market conditions. This characteristic may cause traders to stay away during high slippage conditions brought about by intractable market movements. We propose a Reinforcement Learning (RL) framework to optimize the fees collected on an AMM protocol. In particular, we develop a Q-Learning Agent for Market Making Protocols (QLAMMP) that learns the optimal fee rates and leverage coefficients for a given AMM protocol and maximizes the expected fee collected under a range of different market conditions. We show that QLAMMP is consistently able to outperform its static counterparts under all the simulated test conditions.
Song Jiang, Jie Zhou, Shuang Qiu
The existing studies rarely reveal the reasons for the digital currency price fluctuation from the perspective of internal interaction and contagion. Therefore, to fill this research gap, this paper comprehensively adopts the dynamic conditional correlation (DCC-) GARCH model and wavelet coherence analysis (WTC) to reveal the internal correlation and formation reasons of digital currency price fluctuations. Our research has the following findings: (1) the price fluctuations of digital currency are highly related. Through the observation of the dynamic conditional correlation coefficient graph, it is found that the price fluctuations have a strong time-varying trend, manifested as a ‘contagious’ characteristic. (2) During the outbreak of COVID-19, most digital currencies have shown positive resonance in the short, medium, and long term, suggesting that the COVID-19 pandemic has increased the correlation and contagion of digital currency price fluctuations. (3) In the short term, Bitcoin is the main ‘contagious source’ of digital currency price fluctuation. But in the medium and long term, Ethereum and Ripple, which are closely related to the real economy, have a greater impact and become the new ‘contagious source’. Generally speaking, Bitcoin, Ethereum, and Ripple are the internal causes of instability in the digital currency market. Finally, based on the empirical conclusion, this paper proposes that the digital currency portfolio should be optimized to meet the investment demand; strengthen digital currency regulatory cooperation, and improve regulatory efficiency. Let the digital currency return to the ‘currency’ attribute and serve the real economy.
Amin Izadyar, Shiva Zamani
This paper investigates how changes in investor base is related to idiosyncratic volatility in cryptocurrency markets. For each cryptocurrency, we set change in its subreddit followers as a proxy for the change in its investor base, and find out that the latter can significantly increase cryptocurrencies idiosyncratic volatility. This finding is not subsumed by effects of size, momentum, liquidity and volume and is robust to various measures of idiosyncratic volatility.
Ruize Sun
In 2022, with the implementation of tightening monetary policies by FOMC, US dollar is experiencing a dramatic appreciation in a very short period. Though numerous studies have demonstrated the connection between the traditional currency market, cryptocurrency market, and precious metal market, rare studies are exploring the relationships between the three markets under a special political environment. This paper selects USDCNY exchange rate, gold and silver, and bitcoin as the representatives of three markets and then tests the volatility response of return on gold & silver and return on bitcoin to the change of return on USDCNY exchange rate. By employing impulse response function and ARMA-GARCHX model, the paper verifies the change of exchange rate will exacerbate the volatility of returns on gold & silver and bitcoin significantly, which suggests high risk and uncertainty of the cryptocurrency market and precious metal market in a complex and extreme political environment. Investors and speculators should take prudent investment strategies in such environment.
Vishwas Kukreti
Being archetypal complex systems, financial markets exhibit rich set of dynamics in their interactions. In this paper, we focus on the recently evolved cryptocurrency market as an example of a complex system and analyse the evolution of cross correlation structure of cryptocurrencies in the 5 year period from 2017 to 2022. We observe characteristic correlation structures in the observation time window duration and use these specific structures to cluster the cryptocurrency market in 4 market states.
Qasem Abu Al‐Haija
Bitcoin (BTC) is a distributed virtual paradigm that uses a peer-to-peer network to provide a means of digital money. Bitcoin pricing was changing monthly, and the BTC price observations have been collected since October 2013. In this paper, we propose a neural network-based autoregressive predictive model to forecast the monthly pricing of cryptocurrency bitcoin technology based on 100 historical observations for the bitcoin prices from Oct-2013 to Sep-2021 (in us dollars). Specifically, the proposed scheme uses a nonlinear autoregressive neural network with external input (NARX) by detaining the maximum regression coefficient corresponding to the most prediction accuracy and the least normalized prediction error. The simulation results showed that the highest prediction accuracy for the identified cryptocurrency, bitcoin pricing is 99.1%. The subsequent perdition model was effectively used to anticipate the evolution of forthcoming 12-month data records for the n cryptocurrency bitcoin prices from Oct-2021 to Sep-2022. The forecast values reveal a very slow, linearly developing tendency in the prices of cryptocurrency bitcoin released monthly over the past ten years' records for the global cryptocurrency bitcoin pricing time series.
Pierpaolo Benigno
Abstract Can currency competition affect central banks’ control of interest rates and prices? Yes, it can. In a two-currency world with competing cash (material or digital), the growth rate of the cryptocurrency sets an upper bound on the nominal interest rate and the attainable inflation rate, if the government currency is to retain its role as medium of exchange. In any case, the government has full control of the inflation rate. With an interest-bearing digital currency, equilibria in which government currency loses medium-of-exchange property are ruled out. This benefit comes at the cost of relinquishing control over the inflation rate.
Syed Ali Raza, Komal Akram Khan, Khaled Guesmi, Ramzi Benkraiem
No abstract is available for this record.
Ranjan Aneja, Robert Dygas
Literature review regarding digital currencies and cryptocurrencies in the New Global Financial System - 1
Xiao Li, Linda Du
No abstract is available for this record.
Dimitar Kitanovski, Miroslav Mirchev, Ivan Chorbev, Igor Mishkovski
As of the end of 2013 till now we are witnessing huge volatility and risk in the cryptocurrency market compared to flat currency or stock market. Thus, in this market the portfolio diversification is of big importance in order to reduce volatility and keep the optimal return for the investors. A usual approach for portfolio construction is to keep a balance between returns and volatility, based on their interdependence and individual returns. One way of diversification is employing clustering or community detection algorithms to select a more diverse set of assets. We study the utilization of the Louvain algorithm and affinity propagation for community detection, based on correlation and mutual information between cryptocurrencies, for potential application in portfolio diversification.
David Iheke Okorie, Boqiang Lin
No abstract is available for this record.
Dora Almeida, Andreia Dionísio, Isabel Vieira, Paulo Ferreira
Cryptocurrency investments are often perceived as uncertain and risky. In this study, we assessed if this is indeed the case, using a sample of seven cryptocurrencies and considered a period that encompassed the first real global shock in the life of these relatively new financial assets, the COVID-19 pandemic. Uncertainty was evaluated using Shannon’s symbolic entropy. To measure risk, we use value-at-risk and conditional value-at-risk. The results indicate that, except for Tether, the analyzed cryptocurrencies’ returns exhibited similar patterns of uncertainty and risk. Levels of uncertainty were close to the maximum values, but high uncertainty is not always associated with high risk. During the pandemic crisis, uncertainty increased while risk decreased, suggesting that the considered assets may have safe haven properties.
Misha Perepelitsa
In this paper we give an elementary analysis of economics of Bitcoin that combines the transaction demand by the consumers and the supply of hashrate by miners. We argue that the decreasing block reward will have no significant effect on the exchange rate (price) of Bitcoin and thus the network will be transitioning to a regime where transaction fees will play a bigger part of miners' revenue. We consider a simple model where consumers demand bitcoins for transactions, but not for hoarding bitcoins, and we analyze market equilibrium where the demand is matched with the hashrate supplied by miners. Our main conclusion is that the exchange rate of Bitcoin cannot be determined from the market equilibrium and so our arguments support the hypothesis that Bitcoin price has no economic fundamentals and is free to fluctuate according to the present demand for hoarding and speculation. We point out that increasing fees bear the risk of Bitcoin being outcompeted by its main rival Ethereum, and that decreasing revenues to miners depreciate the perception of Bitcoin as a medium for store value (hoarding demand) which will have effect its exchange rate.
Geumil Bae, Jang Ho Kim
The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.
Sophia Koch, Thomas Dimpfl
No abstract is available for this record.
Xingzhi Qiao, Huiming Zhu, Yiding Tang, Cheng Peng
No abstract is available for this record.
Ditdit Nugeraha Utama
Evaluation of cryptocurrency’s performance is performed questionably. There is no role for computer-model, make such an evaluation process does not have guidance. In this study, a simple decision support model (DSM) based on fuzzy logic was academically constructed to observe the cryptocurrency’s performance. By operating the primary method of fuzzy logic and taking into account three types of parameters (i.e. time-series close price data, daily max-min price, and transaction number), a novel DSM for evaluating the cryptocurrency’s performance was fruitfully executed. Based on three types of real cryptocurrency six-month data (i.e. Bitcoin Ethereum, and Dogecoin), the model could irreversibly expose that Bitcoin has the best performance with 36.39 performance points.
Alberto Partida, Saki Gerassis, Regino Criado, Miguel Romance · 6 authors
No abstract is available for this record.
Yongkil Ahn, Dongyeon Kim
No abstract is available for this record.
Wenjun Feng, Zhengjun Zhang
No abstract is available for this record.
Tarun Chitra, Kshitij Kulkarni
Maximal Extractable Value (MEV) has generally been viewed as a negative, parasitic aspect of economic transactions on blockchains that increases costs for non-strategic users. Recent work has shown that MEV is not always bad for social welfare in crypto networks. In this note, we demonstrate that if rational validators in Proof of Stake (PoS) protocols are able to earn a portion of MEV revenue, by a process we call MEV redistribution, they are disincentivized to unstake and lower economic security. We construct a joint staking-lending dynamical system in which a fraction of MEV revenue is used to increase staking returns. We formally show that this MEV redistribution can avoid bad competitive equilibria between staking and lending in which no users stake under benign conditions on the reward inflation schedule of the protocol, and conduct numerical simulations that demonstrate this. This represents another potentially positive externality of MEV, provided that the mechanism for redistribution is well-designed.
Martin Nedved, Ladislav Krištoufek
No abstract is available for this record.