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3,636 results · page 64 of 152
Yongsheng Yi, Mengxi He, Yaojie Zhang
No abstract is available for this record.
Lanouar Charfeddine, Noureddine Benlagha, Karim Ben Khediri
No abstract is available for this record.
Zhuzhu Wen, Elie Bouri, Yahua Xu, Yang Zhao
No abstract is available for this record.
Isha Narula, Ms. Kriti Dhingra, Ridhima Sharma
Global economic markets are encountering apprehensions and susceptibilities after the pandemic of COVID-19. Investment patterns are becoming restrained because of uncertain global scenarios and reduced GDP worldwide. World economies are moving towards digital era and investors are becoming more open towards the newest forms of investments. Due to the uncertain scenarios, investors globally are looking forward to some lucrative forms of investments and cryptocurrencies are the ray of hope for global investors. The present study is attempt to explore the changing dynamics of cryptocurrencies with the market uncertainties. Volatility of five cryptocurrencies, namely Bitcoin, Ethereum, XRP, Chainlink and Bitcoin Cash are analysed using the Generalised AutoRegressive Conditional Heteroskedasticity Model. Results showed that the investors preferred taking cautious decisions and invested more in famous bitcoin rather than other cryptocurrencies.
Antonio Briola, Tomaso Aste
We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones.
Yu-Min Lian, Jia-Ling Chen, Hsueh-Chien Cheng
Abstract In this study, we predict Bitcoin price trends using the back propagation neural network (BPNN), autoregressive integrated moving average (ARIMA), and generalized autoregressive conditional heteroscedasticity (GARCH) models. Based on principal component analysis (PCA), we extract two new input components for BPNN from Bitcoin’s three-day closing prices, MA5, MA20, daily trading volume, Ether price, and Ripple price. The training set covers the period between September 1, 2015 and March 31, 2020, and the forecasting set covers the period between April 1, 2020 and June 30, 2020. Empirical results reveal (1) the predictive ability of BPNN over that of the ARIMA models; (2) BPNN with two hidden layers is able to predict price trends more precisely than that with only one hidden layer; (3) in terms of time series models, the ARIMA-GARCH family of models demonstrates better predictive performance than ARIMA models; and (4) among the ARIMAGARCH family of models, the ARIMA-EGARCH model is proven to produce the best predictive results on price, and the ARIMA-GARCH model predicts more accurately than the ARIMA-GJR-GARCH model. Specifically, our findings provide a reference on Bitcoin for market participants. JEL classification numbers: C32, C45, C53, G17. Keywords: Bitcoin, Back propagation neural network, Autoregressive integrated moving average, Generalized autoregressive conditional heteroscedasticity, Principal component analysis.
Ajayi F.I., Oloyede A.J., Oluwaleye T.O.
This study examined the relationship between cryptocurrency shocks and exchange rate behaviour in Nigeria. Selected cryptocurrencies for the study are Bitcoin, Ethereum, Litecoin, Ripple and Binance coin which are the most traded cryptocurrencies in Nigeria. Augmented Dickey-Fuller (ADF), Johansen Cointegration and Vector Autoregressive (VAR) tests were used to analyze the monthly data of exchange rate and selected cryptocurrencies for four years (45 months). The result of the cointegration test revealed the existence of a long-run relationship among the variables. ECM result showed that about 6% of the short-run disequilibrium are being corrected and integrated into the long-run equilibrium relationship. In addition, the Variance Decomposition result showed that Ripple has the highest variations to exchange rate in the short and long runs. The present value of exchange rate adjusts slightly to changes in cryptocurrency. Ripple and Bitcoin have the highest shocks on the exchange rate. Therefore, monetary authorities should give adequate attention to cryptocurrency transactions and make policy decisions on how to reduce the prevailing high exchange rate in Nigeria by integrating crypto transactions in their systems. Transaction in cryptocurrency is still at the early stage, especially in Nigeria; only five years data can be gotten on commonly traded cryptocurrencies in Nigeria. This is a limitation to the study in terms of the number of cryptocurrencies used in the study. More cryptocurrencies can be included in future studies.
Matin Keramiyan, Korhan K. Gökmenoğlu
Purpose This paper aims to examine the predictive power of the volume of Economic Uncertainty Related Queries and the Macroeconomic Uncertainty Index on the Bitcoin returns. Design/methodology/approach Data consists of 118 monthly observations from September 2010 to June 2020. Due to the departure of series from Gaussian distribution and the existence of outliers, the authors use the quantile analysis framework to investigate the persistency of the shocks, the long-run relationships and Granger causality among the variables. Findings This research provides several important findings. First, the substantial differences between conventional and quantile test results stress the importance of the method selection. Second, throughout the conditional distribution of the series, stochastic properties of the variables, long-run and the causal relationships between the variables might be significantly different. Third, rich information provided by the quantile framework might help the investors design better investment strategies. Originality/value This study differs from the previous research in terms of variable selection and econometric methodology. Therefore, it presents a more comprehensive framework that suggests implications for empirical researchers and Bitcoin investors.
Tao Yin, Yiming Wang
This paper mainly studies the market nonlinearity and the prediction model based on the intrinsic generation mechanism (chaos) of Bitcoin’s daily return’s volatility from June 27, 2013 to November 7, 2019 with an econophysics perspective, so as to avoid the forecasting model misspecification. Firstly, this paper studies the multifractal and chaotic nonlinear characteristics of Bitcoin volatility by using multifractal detrended fluctuation analysis (MFDFA) and largest Lyapunov exponent (LLE) methods. Then, from the perspective of nonlinearity, the measured values of multifractal and chaos show that the volatility of Bitcoin has short-term predictability. The study of chaos and multifractal dynamics in nonlinear systems is very important in terms of their predictability. The chaos signals may have short-term predictability, while multifractals and self-similarity can increase the likelihood of accurately predicting future sequences of these signals. Finally, we constructed a number of chaotic artificial neural network models to forecast the Bitcoin return’s volatility avoiding the model misspecification. The results show that chaotic artificial neural network models have good prediction effect by comparing these models with the existing Artificial Neural Network (ANN) models. This is because the chaotic artificial neural network models can extract hidden patterns and accurately model time series from potential signals, while the benchmark ANN models are based on Gaussian kernel local approximation of non-stationary signals, so they cannot approach the global model with chaotic characteristics. At the same time, the multifractal parameters are further mined to obtain more market information to guide financial practice. These above findings matter for investors (especially for investors in quantitative trading) as well as effective supervision of financial institutions by government.
You Liang, A. Thavaneswaran, Alex Paseka, Wei Qiao · 6 authors
Pairs trading strategies are constructed based on exploiting mean reversion in security prices, which have been demonstrated to perform well for stocks. However, their performance is not widely studied for cryptocurrencies, which are usually discerned as inefficient and unpredictable. One significant advantage of pairs trading is that potential profits can be generated regardless of the overall market movement. The pairs trading has the potential to be profitable for cryptocurrencies in bear markets and with intraday data. Kalman filter (KF) algorithms are popular for pairs trading to update the hedge ratio dynamically. They reduce the arbitrariness in parameter optimization by putting constraints on the parameter space. However, a major drawback is that the innovation volatility estimate calculated by using a KF algorithm is always affected by the initial values and outliers. An effective resilient filtering approach to estimate the innovation volatility is presented in this paper for cryptocurrencies. This paper presents rolling regression pairs trading strategies, traditional KF pairs trading strategies and resilient filter pairs trading strategies. The proposed trading strategies have been evaluated through some experiments on hourly Bitcoin USD and Ethereum USD prices and it is shown that the proposed resilient filter trading strategy is much more stable to initial values than the traditional KF trading strategy.
Guillermo Angeris, Tarun Chitra, Alex Evans
In this paper, we give a simple but very general definition of 'price stability' for a class of markets. This class of markets includes the popular constant function market makers (CFMMs) such as Uniswap, Curve, and Balancer, used extensively in decentralized finance (DeFi), which now have daily trading volumes in the billions of dollars. We show that our definition of price stability is deeply connected to the curvature of the trading function used in the CFMM, making the folk intuition that "flatter CFMMs are more price stable" more concrete. We also show that this definition gives sufficient conditions for the profitability of liquidity providers, and, similar to the classical market microstructure literature, gives bounds on the edge of informed traders and bounds on the losses of liquidity providers. We also show how these bounds help explain some of the behaviors observed in decentralized finance in the second half of 2020, including the rise of 'yield farming ' and 'vampire attacks.'
Ruchita Verma, Dhanraj Sharma, Shiney Sam
Cryptocurrency as a financial asset has emerged as a fad among investors, academicians and policymakers alike. In a financial purview, this study intends to empirically test the behaviour of the cryptocurrency return, inferring its market efficiency. For this purpose, daily data of five cryptocurrencies (Bitcoin, Ethereum, Litecoin, Tether and Ripple) have been collected from 1 January 2016 to 31 March 2021 to investigate the well-known financial theory of random walk hypothesis for this young market. To provide statistical evidence and ensure the robustness of results, analysis is performed using the variance ratio test, augmented Dickey–Fuller test, Philip–Perron test, Breusch–Godfrey serial correlation LM test and ARIMA model. The statistical results illustrated strong evidence refuting the presence of the random walk hypothesis in this emerging market, thus implying inefficiency in the cryptocurrency market. Furthermore, the absence of random walk in the cryptocurrency makes this financial asset predictable, giving investors an arbitrage edge to earn abnormal gains using trading strategies, which is euphoria.
Tianquan Liu, Yiming Wang, Yu Yan
Bitcoin market had a significant momentum phenomenon before the launch of Futures, and then it turned into an insignificant reversal effect. After Covid-19 appeared, the momentum effect and reversal effect disappeared. The advent of bitcoin futures has increased how investors respond to information. With the outbreak of COVID-19, investor interest in Bitcoin as a safe-haven asset has increased the effectiveness of the price. We estimate the speed of signal diffusion in the bitcoin market, and the results support that effective response to information is the essential mechanism for the disappearance of momentum effect.
Kuo‐Shing Chen, Shen‐Ho Chang
This paper aims to investigate and measure Bitcoin and the five largest stablecoin market volatilities by incorporating various range-based volatility estimators to the BEKK- GARCH and Copula-DCC-GARCH models. Specifically, we further measure Bitcoins’ volatility related to five major stablecoins and examine the connectedness between Bitcoin and the stablecoins. Our empirical findings document that the connectedness between Bitcoin and stablecoin market volatility behaviors exhibits the presence of stable interconnection. This study is of particular importance since it is crucial for market participation in the ongoing crypto assets to be informed about both the volatility patterns of major cryptocurrencies and the relative volatility of Bitcoin against the stablecoin markets. Eventually, we find that there is no systematic evidence for the various parity deviations of the stablecoins that are profoundly impacted by Bitcoin volatility. Thus, Bitcoin and the largest stablecoin Tether could stabilize together. However, Bitcoin shall not be generalized to other stablecoins in terms of stability results.
Jordan T. Jones, Doga Demirel
With time-series data being prevalent everywhere, there is a need to predict this data accurately. This kind of data includes weather data, financial data such as stock price, and cryptocurrency price. Most of the trades in the stock market in this day and age are being made using artificial intelligence. An estimated 50% of trades were done using an algorithm, which increased to 60% in 2020 [1]. This highlights the demand for reliable and accurate predictions. The prediction of the price is very challenging. Some success has been seen when predicting stock prices, but not many studies have been done on cryptocurrency. Cryptocurrency, specifically Bitcoin, has seen a substantial increase in popularity, and the price has reflected this popularity. The price also follows patterns specifically when reaching new all-time highs. In this work, an Artificial intelligence is created and trained on the previous data to observe these patterns and predict the next price. The artificial intelligence chosen for this subject is Long short-term memory (LSTM). LSTMs are capable of finding patterns in time series data. LSTM solves the vanishing gradient problem present in the RNN (Recurrent Neural Network). The Market Price of Bitcoin is used as input here. The data values for input range from 20,000 up to 65,000 in testing. Once an optimal starting point is found, there is an 80/20 split of data, 80 percent of the data is used for training and 20 is used for testing. With the data being split, one of the most important jobs is figuring out the optimal lags (how far back into the past) when used to predict values. This range for this experiment is set to ten previous price days. Epochs (number of iterations) and Batch size (how much of the training data is used per epoch) are tested at different values to find optimal solutions. With batch size values such that batchSize ∈ {20, 21…26} and epochs such that epochs ∈ {10, 20….70}. Overfitting is hard to detect and thus can be an issue with too many epochs and smaller batch sizes (smaller means more of the training data is used). Too little and the LSTM will not learn the data patterns and thus will not have good accuracy. This is why different configurations are used in the experiment to maximize accuracy. This LSTM was used to achieve a Mean Absolute Percentage Error score of 3.23% and a Root Mean Squared Error score of 1892.87 when predicting next-day prices throughout 350.
Horst Treiblmaier
The disruptive impact of blockchain technologies can be felt across numerous industries as it threatens to disrupt existing business models and economic structures. To better understand this impact, academic researchers regularly apply well-established theories and methods. The vast majority of these approaches are based on multivariate methods that rely on average behavior and treat extreme cases as outliers. However, as recent history has shown, current developments in blockchain and cryptocurrencies are frequently characterized by aberrant behavior and unexpected events that shape individuals’ perceptions, market behavior, and public policymaking. In this paper, I apply various scenario tools to identify such extreme scenarios and illustrate their underlying structure as bundles of interdependent factors. Using the case of Bitcoin, I illustrate that the identification of extreme positive and negative scenarios is complex and heavily depends on underlying economic assumptions. I present three scenarios in which Bitcoin is characterized as a financial savior, as a severe threat to economic stability, or as a substitute to overcome several shortcomings of the existing financial system. The research questions that can be derived from these scenarios bridge behavioral and design science research and provide a fertile ground for impactful future research.
Nicola Dimitri
In the paper we investigate consensus formation, from an economic perspective, in a Proof-of-Stake (PoS) based platform inspired by the Algorand blockchain. In particular, we consider PoS in relation to governance, focusing on two main issues. First we discuss alternative sampling schemes, which can be adopted to select voting committees and to define the number of votes of committee members. The selection probability is proportional to one’s stake and increases with it. Participation in governance allows users to affect the platform’s decisions as well as to obtain a reward. Then, based on such preliminary analysis, we introduce a microeconomic model to investigate the optimal stake size for a generic user. In the model we conceptualize an optimal stake, for a user, as striking the balance between having Algos immediately available for transactions and setting aside currency units to increase the probability of becoming a committee member. Our main findings suggest that the optimal stake can be quite sensitive to the user’s preferences and to the rules for selecting committees. We believe the findings may support policy decisions in PoS based platforms.
Kwamie Dunbar, Johnson Owusu-Amoako
No abstract is available for this record.
Daniele Bianchi, Mykola Babiak, Alexander Dickerson
No abstract is available for this record.
Juan Dempere, Zakea Ali El-Agure, Deni Memić
this study aims to analyze the impact of data selection to train machine learning models and forecast Bitcoin prices. Specifically, we train elastic net regularization models using two datasets with almost identical total observations. One dataset emphasizes years of observations (depth) over total variables, while the second one emphasizes the number of variables (width) over years of data. Our results suggest that the dataset with more extended historical time series and fewer variables provides a lower forecasting error than the dataset with shorter time series and more variables. Our results may be helpful to practitioners looking to identify data selection strategies to train ML-based forecasting models.
Yakup Arı
Volatility is an important concept for identifying and predicting the risk of financial products. The aim of the study is to determine the most appropriate discrete model for the volatility of Bitcoin returns using the discrete-time GARCH model and its extensions and compare it with the Lévy driven continuous-time GARCH model. For this purpose, the volatility of Bitcoin returns is modeled using daily data of Bitcoin / United States Dolar exchange rate. By comparing discrete-time models according to information criteria and likelihood values, the All-GARCH model with Johnson's-SU innovations is found to be the most adequate model. The persistence of the volatility and half-life of the volatility of the returns are calculated according to the estimation of the discrete model. This discrete model has been compared with the continuous model in which the Lévy increments are derived from the compound Poisson process using various error measurements. As a conclusion, it is found that the continuous-time GARCH model shows a better performance to predict the volatility.
Zahra Shahriari, Fahimeh Nazarimehr, Karthikeyan Rajagopal, Sajad Jafari · 6 authors
No abstract is available for this record.
Mustafa Disli, Fatima Abd Rabbo, Thibault Leneeuw, Ruslan Nagayev
Binance, the largest cryptocurrency exchange by traded value, relocated from Hong Kong (origin market) to Malta (destination market). This study exploits this relocation event by examining the comovement of Binance's native token with the native tokens of other cryptocurrency exchanges in the origin and destination markets. Using multivariate regression analysis, our results show that Binance experienced a significant decline in comovement with its origin market after moving to Malta. The results are less evident for the destination market; however, an increase in comovement immediately after the relocation of Binance is notable.
Rajat Kumar Rathore, Deepti Mishra, Pawan Singh Mehra, Om Pal · 8 authors
No abstract is available for this record.