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3,636 results ¡ page 85 of 152
Gianna FigĂ âTalamanca, Sergio M. Focardi, Marco Patacca
No abstract is available for this record.
Emna Mnif, Anis Jarboui
Unlike conventional cryptocurrencies, Islamic ones are new technologies backed by tangible assets and are characterised by their fundamental values. After the COVID-19 outbreak, cryptocurrency responses have shown different behaviour to stock market reactions. However, there is a lack of studies on the efficiency of Islamic and green cryptocurrencies during the pandemic. This paper attempts to analyse the behaviour of three typical families of cryptocurrencies (conventional, Islamic, and green) extracted according to their availability in daily frequencies during COVID-19. For this purpose, their efficiency levels are studied before and after the outbreak by employing multifractal detrended fluctuation analysis (MFDFA) to make the best predictions and strategies. The inefficiency of the cryptocurrencies is assessed through a magnitude of long-memory (MLM) efficiency index, and the impact of COVID-19 on their efficiency is evaluated. The primary results show that HelloGold was the most efficient market before the COVID-19 outbreak and that subsequently Ethereum has been the most efficient. In addition, the findings reveal that the cryptocurrency reactions are not similar and show more resilience in the Ethereum and Litecoin markets than in other cryptocurrency markets. The main contribution of this study is the evaluation of the impact of COVID-19 on the various classes of crypto money. This work has practical implications, as it provides new insights into trading opportunities and market reactions. Moreover, he work has theoretical implications based on its evaluation of three distinct models from different doctrine viewpoints.
Kun Duan, Zeming Li, Andrew Urquhart, Jinqiang Ye
No abstract is available for this record.
Konstantin Häusler, Wolfgang Karl Härdle
We model the dynamics of the cryptocurrency (CC) asset class via a stochastic volatility with correlated jumps (SVCJ) model with rolling-window parameter estimates. By analyzing the time-series of parameters, stylized patterns are observable which are robust to changes of the window size and supported by cluster analysis. During bullish periods, volatility stabilizes at low levels and the size and volatility of jumps in mean decreases. In bearish periods though, volatility increases and takes longer to return to its long-run trend. Furthermore, jumps in mean and jumps in volatility are independent. With the rise of the CC market in 2017, a level shift of the volatility of volatility occurred. All codes are available on Quantlet.com.
Jiahua Xu, Nazariy Vavryk, Krzysztof Paruch, Simon Cousaert
As an integral part of the Decentralized Finance (DeFi) ecosystem, Automated Market Maker (AMM) based Decentralized Exchanges (DEXs) have gained massive traction with the revived interest in blockchain and distributed ledger technology in general. Most prominently, the top six AMMs -- Uniswap, Balancer, Curve, Dodo, Bancor and Sushiswap -- hold in aggregate 15 billion USD worth of crypto-assets as of March 2021. Instead of matching the buy and sell sides, AMMs employ a peer-to-pool method and determine asset price algorithmically through a so-called conservation function. Compared to centralized exchanges, AMMs exhibit the apparent advantage of decentralization, automation and continuous liquidity. Nonetheless, AMMs typically feature drawbacks such as high slippage for traders and divergence loss for liquidity providers. In this work, we establish a general AMM framework describing the economics and formalizing the system's state-space representation. We employ our framework to systematically compare the mechanics of the top AMM protocols, deriving their slippage and divergence loss functions.
Jiahua Xu, Krzysztof Paruch, Simon Cousaert, Yebo Feng
As an integral part of the decentralized finance (DeFi) ecosystem, decentralized exchanges (DEXs) with automated market maker (AMM) protocols have gained massive traction with the recently revived interest in blockchain and distributed ledger technology (DLT) in general. Instead of matching the buy and sell sides, automated market makers (AMMs) employ a peer-to-pool method and determine asset price algorithmically through a so-called conservation function. To facilitate the improvement and development of automated market maker (AMM)-based decentralized exchanges (DEXs), we create the first systematization of knowledge in this area. We first establish a general automated market maker (AMM) framework describing the economics and formalizing the system's state-space representation. We then employ our framework to systematically compare the top automated market maker (AMM) protocols' mechanics, illustrating their conservation functions, as well as slippage and divergence loss functions. We further discuss security and privacy concerns, how they are enabled by automated market maker (AMM)-based decentralized exchanges (DEXs)' inherent properties, and explore mitigating solutions. Finally, we conduct a comprehensive literature review on related work covering both decentralized finance (DeFi) and conventional market microstructure.
Ahmet Ĺensoy, Thiago Christiano Silva, Shaen Corbet, Benjamin Miranda Tabak
We examine the high-frequency return and volatility of major cryptocurrencies and reveal that spillovers among them exist. Our analysis shows that return and volatility clustering structures are distinct among different cryptocurrencies, suggesting that return and volatility might have different spillover patterns. Further investigation via minimal spanning trees points out that BTC, LTC and ETH are the most relevant cryptocurrencies in general, serving as connection hubs for linking many other cryptocurrencies. However, their role is challenged lately, potentially due to the increased usage of other cryptocurrencies in time.
Ahmet Faruk Aysan, Asad Ul Islam Khan, Humeyra Topuz, Ahmet Semih TunalÄą
This paper explores the applicability of universal cryptocurrency exchange by analyzing crypto exchanges of Binance, Latoken, Kucoin and Qash, which also have their own cryptocurrencies in the crypto market. Results of the recursive Johansen cointegration test proved that even though all of the cryptocurrencies have cointegration among each other, Binance positively disassociated itself from the others after it moved to Malta on 23 March 2018. Based on the daily prices of cryptocurrencies over the period from 6 November 2017 to 10 November 2019, taken from coinmarketcap, we conclude that Binance can be considered as a survival of the fittest among all of the crypto exchanges in this natural experiment.
Vijay Kumar Shrotryia, Himanshi Kalra
Purpose With the unprecedented growth of digitalization across the globe, a new asset class, that is cryptocurrency, has emerged to attract investors of all stripe. The novelty of this newly emerged asset class has led researchers to gauge anomalous trade patterns and behavioural fallacies in the crypto market. Therefore, the present study aims to examine the herd behaviour in a newly evolved cryptocurrency market during normal, skewed, Bitcoin bubble and COVID-19 phases. It, then, investigates the significance of Bitcoin in driving herding bias in the market. Finally, the study gauges herding contagion between the crypto market and stock markets. Design/methodology/approach The study employs daily closing prices of cryptocurrencies and relevant stocks of S&P 500 (USA), S&P BSE Sensex (Index) and MERVAL (Argentina) indices for a period spanning from June 2015 to May 2020. Quantile regression specifications of Chang et al.â s (2000) absolute deviation method have been used to locate herding bias. Dummy regression models have also been deployed to examine herd activity during skewed, crises and COVID-19 phases. Findings The descriptive statistics reveal that the relevant distributions are leptokurtic, justifying the selection of quantile regression to diagnose tails for herding bias. The empirical results provide robust evidence of crypto herd activity during normal, bullish and high volatility periods. Next, the authors find that the assumptions of traditional financial doctrines hold during the Bitcoin bubble. Further, the study reveals that the recent outbreak of COVID-19 subjects the crypto market to herding activity at quantile ( t ) = 0.60. Finally, no contagion is observed between cryptocurrency and stock market herding. Practical implications Drawing on the empirical findings, it is believed that in this age of digitalization and technological escalation, this new asset class can offer diversification benefits to the investors. Also, the crypto market seems quite immune to behavioural idiosyncrasies during turbulence. This may relieve regulators of the possible instability this market may pose to the entire financial system. Originality/value The present study appears to be the first attempt to diagnose leptokurtic tails of relevant distribution for crypto herding in the wake of two remarkable events: the crypto asset bubble (2016â2017) and the outbreak of coronavirus (early 2020).
ZiâYi Guo
No abstract is available for this record.
Sumit Biswas, Mohandas V. Pawar, Sachin L. Badole, Nachiket Galande ¡ 5 authors
This rise in cryptocurrencies' value has contributed to the decentralization of authority, lowering control amongst countries. The wide price range of digital currencies highlights the need for reliable preparation for predicting the currency's price. A new model is a situation in which this paper presents a new way of forecasting digital value for money by considering several variables, such as stock market capitalization, volume, distribution, and high-end delivery. To include training results, active LSTM networks, and an overview of long-term organizations are considered. The proposed technique is used for the benchmark data sets. The results indicate the efficiency of the forecasting of digital currency.
Michael Demmler, Amilcar Orlian FernĂĄndez DomĂnguez
This paper examines historical Bitcoin price data together with the price data of a well-known and generally accepted historical asset price bubble (the 1720 South Sea Bubble) with the aim of identifying possible similarities. In order to find empirical evidence of speculative bubble tendencies, the article analyses distribution moments and autoregressive models of time series of both assets. Results show that historical daily prices of both assetsâtaking into account one year before and one year after the maximum price levelâclearly show the two phases of bubble expansion and subsequent crash. Furthermore, various similarities between the South Sea Bubble and Bitcoin can be found in descriptive statistics, such as mean of return, standard deviation, and skewness. Statistical tests also show several explosive moments in the time series of the South Sea Company and Bitcoin returns, which implies that both assets exhibit more than one financial bubble.
Min-Yuh Day, Paoyu Huang, Yirung Cheng, Yin-Tzu Lin ¡ 5 authors
We explore the profitability of day trading Bitcoin futures following diverse consecutive 3 bullish (bearish) one-minute candlesticks and then adopt stop-loss or take-profit only as exits. We reveal that different from our cognition, adopting take-profit strategies would have a notable average positive profit per trade (APPT), which is robust by employing the upward trend out-of-sample data different from the downward trend in-sample data. We infer that such impressive findings might result from temporary rising (falling) prices likely manipulated for appealing to investors pursuing long (short) positions. As a result, investors may realize profit instead of suffering loss frequently by adopting take-profit exits since mean reversion might often occur after such manipulation. Moreover, we argue that stop-loss might be redundant for day trading such futures because stop-loss seems enclosed in the mechanism of day trading.
Mahdiye Rahmani Cherati, Abdorrahman Haeri, Seyed Farid Ghannadpour
Recently, the deep learning architecture has been used with an increasing rate for forecasting in financial markets. In this paper, the LSTM model is used to forecast the daily closing price direction of the BTC/USD. Both model accuracy and the profit or loss of the trades made based on the proposed model are analyzed. In addition, the effects of the MACD indicator and the input matrix dimension on forecasting accuracy are evaluated. The potential risks and actual risks encountered by the trader who trades based on the proposed model were also analyzed. The obtained results indicate that the optimization of the LSTM parameters using the Bayesian optimization model has enhanced the modelâs accuracy. The results obtained from analyzing the drawdown and reward/risk resulting from the trades made based on the model show that the model enables the trader to trade with peace of mind due to the low level of actual risks and potential risks.
Paolo De Angelis, Roberto De Marchis, Mario Marino, Antonio Luciano Martire ¡ 5 authors
Abstract In this paper, we come up with an original trading strategy on Bitcoins. The methodology we propose is profit-oriented , and it is based on buying or selling the so-called Contracts for Difference, so that the investorâs gain, assessed at a given future time t , is obtained as the difference between the predicted Bitcoin price and an apt threshold. Starting from some empirical findings, and passing through the specification of a suitable theoretical model for the Bitcoin price process, we are able to provide possible investment scenarios, thanks to the use of a Recurrent Neural Network with a Long Short-Term Memory for predicting purposes.
Gil Cohen
No abstract is available for this record.
Masao Kumamoto, Juanjuan Zhuo
We investigate the financial assets status (diversifier, hedge and safe haven) of Bitcoin and gold against the world and U.S. stock markets. We employ the copula-DCC approach to consider the tail dependence between Bitcoin or gold return and stock return. Our results indicate that Bitcoin is a weak hedge against the world and U.S. stock markets, while gold is a diversifier against the world stock market, but a strong hedge against the U.S. stock market. We also estimate the dynamic conditional betas and find that the returns of Bitcoin and gold are not sensitive to changes in the value of market portfolio. Moreover, we employ the threshold model to investigate whether there exist contagion effects between Bitcoin or gold market and stock markets. Our results show that the increase in market uncertainty weakens the role of Bitcoin as a weak hedge and Bitcoin becomes a diversifier, while it changes the role of gold as a diversifier into a hedge or a safe haven. The above results mean that although Bitcoin is called as ââŹĹnew goldâ⏠, the financial assets status of Bitcoin and gold are different.
Emna Mnif, Anis Jarboui
Purpose Unlike previous crisis where investors tend to put their assets in safe havens like gold, the recent coronavirus pandemic is characterised by an increase in the Bitcoin purchasing described as risk heaven. This paper aims to analyse the Bitcoin dynamics and the investor response by focusing on herd biases. Therefore, the main objective of this work is to study the degree of efficiency through multifractal analysis in order to detect herd behaviour leading to build the best predictions and strategies. Design/methodology/approach This paper develops a novel methodology that detects the presence of herding biases and assesses the inefficiency of Bitcoin through an inefficiency index (MLM) by using statistical indicators defined by measures of persistence. This study, also, investigates the nonlinear dynamical properties of Bitcoin by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA) leading to deduce the effect of COVID-19 on the Bitcoin performance. Besides, this work performs an event study to capture abnormal changes created by COVID-19 related events capable to analyse the Bitcoin market response. Findings The empirical results of the generalized Hurst exponent GHE estimation indicates that Bitcoin is multifractal before this pandemic and becomes less fractal after the outbreak. Using an efficiency index (MLM), Bitcoin is found to be more efficient after the pandemic. Based on the Hausdorff topology, the authors showed that this pandemic has reduced the herd bias. Research limitations/implications The uncertainty of COVID-19 disease and the lasting of its duration make it difficult to make the best prediction. Practical implications The main contribution of this study is the evaluation of the Bitcoin value after the COVID19 outbreak. This work has practical implications as it provides new insights on trading opportunities and social reactions. Originality/value To the authorsâ knowledge, this work represents the first study that analyses the Bitcoin response to different events related to COVID-19 and detects the presence of herding behaviour in such a crisis.
Luis Lorenzo
Since the appearance of Bitcoin, cryptocurrencies have experienced enormousgrowth not only in terms of capitalization but also in number. As a result, thecryptocurrency market can be an attractive arena for investors as it offers manypossibilities, but a difficult one to understand as well. In this work, we aim tosummarize and segment the whole cryptocurrency market in 2018 with the helpof data analysis tools. We will use three different partitional clustering algorithmseach of them using a different representation for cryptocurrencies, namely: yearlymean and standard deviation of the returns, distribution of returns, and timeseries of returns. Since each representation will provide a different andcomplementary perspective of the market, we will also explore the combination ofthe three clustering results to obtain a fine-grained analysis of the main trends ofthe market. Finally, we will analyse the association of the clustering results withother descriptive features of the cryptocurrencies, including the age, technologicalattributes, and financial ratios derived from them. This will help to enhance theprofiling of the clusters with additional insights. As a result, this work offers adescription of the market and a methodology that can be reproduced by investorsthat want to understand the main trends on the market and that look forcryptocurrencies with different financial performance.
Larisa Yarovaya, Roman Matkovskyy, Akanksha Jalan
No abstract is available for this record.
Yash Indulkar
This paper consists of cryptocurrency prediction and analysis using different algorithms, the major cryptocurrency took into account for analysis and prediction are Bitcoin (BTC), Ethereum (ETH), Chainlink (LINK), Bitcoin Cash (BTC), XRP (XRP). Nowadays, investing in cryptocurrency has become a major deal, with huge cash flow and billions of industries which has taken over the small industry that was over the past. With this investment, it is important to understand the high & low of a particular cryptocurrency and what output will be generated with such decisions. Prediction of cryptocurrencies is tangible and requires lots of understanding regarding the flow of money on daily basis. The machine learning industry has advanced to a great extent and it would further do, this advancement has led us to a bigger problem-solving technique, that is prediction of data or analysis of trend which can be in any format. The format in this paper is a time series analysis of the daily high-low-close of digital currency. The algorithms used for such analysis is LSTM (Long Short-Term Memory) which is part of Deep Learning and further Fbprophet which is an Auto Machine Learning for prediction is used. The metric used for the analysis of the algorithm is MAE (Mean Absolute Error). The programming language used is Python, which solves the majority of use cases.
Sofia Anyfantaki, Stelios Arvanitis, Nikolas Topaloglou
No abstract is available for this record.
Ahmed Jeribi, Mohamed Fakhfekh
No abstract is available for this record.
Xin-Jian Jiang, Xiao Fan Liu
CryptoKitties was the first widely recognized blockchain game. Players could own, breed, and trade kitties, which are the only prop in the game. The game gained explosive growth upon its release but quickly collapsed in a short time. This study analyzes its entire player activity history for the first time in literature and tries to find the reasons for the rise and fall of this first blockchain game mania. First, we extracted the five million transaction records among 100 thousand addresses involved in CryptoKitties in the past three years. Based on the numbers of addresses involved in the game each day, we divide the game progress into four stages: the primer, the rise, the fall, and the serenity. We construct a temporal kitty ownership transfer network and analyze the varying network parameters in the four stages. We find that a large number of players poured in during the 10th and 18th days since the game release and quickly exited in the following month. Since then, a few big players have gradually dominated the game, concentrating the game resources. Through further analysis, we find that the main reason for the rapid increase in the game popularity was the increase of public attention by media outlets, while the reasons for the rapid decline in the game popularity include the oversupply of kitties, the decreasing of player income, a widening gap between the rich and poor players, and the limitations of blockchain systems. Based on these observations, we advise on the further blockchain game design: (1) to finely control the production of props and avoid an oversupply, (2) to balance the gaming cost and revenue and protect the enjoyment of players, (3) to narrow down the gap between rich and poor and create an equal gaming community, (4) to consider the limitations of blockchain systems in their game designs.