This study investigates the asymmetric shock transmission mechanisms between seven large cryptocurrencies and crude oil at different market conditions across time. Wavelet technique was used to decompose the daily return series of the assets into wavelet scales to capture trading horizons. We applied quantile regression (QR) and quantile-in-quantile Regression (QQR) on the decomposed series to capture the bear (bull) market conditions. Applying the QR, we found Ethereum, Steller, Ripple and Monero as hedges for oil market volatility at all market regimes from medium to long terms. The QR undermined the hedging properties of Bitcoin, Litecoin and Das, suggesting possible spread of market disruptions from these markets to crude oil market. We observe from QQR that the assets have negative influence on each other at bear market but positive influence at bull market across time, signifying hedging possibilities for both assets in bear market. The significance of our finding is strengthened by the recent rise in the market share of cryptocurrencies.
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.
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.
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 Bitcoinâs 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.
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.
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.
This paper mainly studies the market nonlinearity and the prediction model based on the intrinsic generation mechanism (chaos) of Bitcoinâs daily returnâs 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 returnâs 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.
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.'
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 Bitcoinsâ 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.
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 individualsâ 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.
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 oneâs stake and increases with it. Participation in governance allows users to affect the platformâs 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 userâs preferences and to the rules for selecting committees. We believe the findings may support policy decisions in PoS based platforms.
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 LĂŠvy 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 LĂŠvy 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.
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.
In this study, the dependence between Bitcoin (BTC) and economic policy uncertainty (EPU) of USA and China is estimated by applying the latest methodology of quantile cross-spectral dependence. Daily data comprising a total of 1947 observations and covering the period of 1 October 2013 to 31 January 2019 are used in this study. The findings indicate that a positive return interdependence between BTC and EPU is high in the short term, and this dependence decreases as investment horizons increase from weekly to yearly. The information on the time-varying and timeâfrequency structure of interdependence is also extracted by applying wavelet coherence analysis. The estimated results of wavelet coherence suggest that the correlation between BTC and EPU is positive during a short-term investment horizon. Finally, the frequency domain Breitung and Candelon causality test is applied, and results show the evidence of insignificant causality between Bitcoin and EPU. Overall, the findings highlight the diversification benefits of Bitcoin during the period of uncertainty.
Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the "maximization-minimization" approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature.
The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of 'proof-of-work' with 'proof-of-space' or 'proof-of-stake', although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage.
Michael Demmler, Amilcar Orlian FernĂĄndez DomĂnguez
This article explores the concepts of cryptocurrencies and speculative bubbles, as Bitcoinâs price behaviour shares characteristics with speculative bubbles that have occurred in recent years. Using a quantitative research design, the study examines daily market prices for the period between 2013 and 2019. Statistical moments, return stationarity, TARCH-type model estimations and Supremum Augmented Dickey-Fuller and Generalised Supremum Augmented Dickey-Fuller tests are analysed. We find evidence for multiple speculative bubble tendencies in Bitcoin prices caused by speculation, which reached their maximum at the end of 2017. Our results are in line with recent studies, which characterise Bitcoin as both highly speculative and vulnerable to financial bubbles.
Abstract The Bitcoin futures market has grown rapidly since its 2017 introduction. Along with enabling institutional traders to access a regulated cryptocurrency product, futures provide a means to improve market efficiency by shorting Bitcoin. We examine trading behavior in Bitcoin futures utilizing the Commodity Futures Trading Commission Commitment of Traders report. Leveraged money traders tend to hold the largest positions, be net short, and their trading behavior plays a key role in the Bitcoin futures market. Our empirical results show that leveraged money traders display market timing ability, largely by adjusting their short positions. It seems that other trader types follow this âsmart moneyâ in adjusting their own positions in subsequent periods. We also demonstrate that it is possible to construct profitable trading strategies based on observed variations in leveraged money positions.
Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.
Overview The aim of the International Conferences "Economic Scientific Research-Theoretical, Empirical and Practical Approaches"- (ESPERA), initiated in 2013 by the "Costin C. KiriČescu" National Institute for Economic Research (NIER) within the Romanian Academy is to present and evaluate the economic scientific research portfolio, to argue and substantiate the Romanian development strategies - including European and global best practices, to provide an opportunity for researches, practitioners, and academics interested in economic scientific research, both theoretical, practical and empirical discuss and exchange insightful research ideas. The 7th edition of the International Conferences âEconomic Scientific Research-Theoretical, Empirical and Practical Approachesâ- (ESPERA), under the title â30 Years of Inspiring Academic Economic Research â From the Transition to a Market Economy to the Interlinked Crises of 21st Centuryâ was organized virtually during 26th -27th November 2020, In Bucharest, Romania. The event, dedicated to the 30th anniversary of NIER and its economic research network of its return under the auspices of the Romanian Academy, will include a scientific program of wide diversity initiatives, bringing together researchers from all NIER institutes and centers, members of the Romanian Academy, Romanian academic researchers and also guests from other countries. The
With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors. In this paper, we focus on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time, which we refer to as the target coin prediction task. Firstly, we conduct a comprehensive study of the latest 709 P&D events organized in Telegram from Jan. 2019 to Jan. 2022. Our empirical analysis reveals some interesting patterns of P&Ds, such as that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity. Here channel refers a form of group in Telegram that is frequently used to coordinate P&D events. This observation inspires us to develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&D event history into a sequence representation via the positional attention mechanism to enhance the prediction accuracy. Positional attention helps to extract useful information and alleviates noise, especially when the sequence length is long. Extensive experiments verify the effectiveness and generalizability of proposed methods. Additionally, we release the code and P&D dataset on GitHub: https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency, and regularly update the dataset.
Ali Raheman, Anton Kolonin, Igors Fridkins, Ikram Ansari ¡ 5 authors
In this paper, we explore the usability of different natural language processing models for the sentiment analysis of social media applied to financial market prediction, using the cryptocurrency domain as a reference. We study how the different sentiment metrics are correlated with the price movements of Bitcoin. For this purpose, we explore different methods to calculate the sentiment metrics from a text finding most of them not very accurate for this prediction task. We find that one of the models outperforms more than 20 other public ones and makes it possible to fine-tune it efficiently given its interpretable nature. Thus we confirm that interpretable artificial intelligence and natural language processing methods might be more valuable practically than non-explainable and non-interpretable ones. In the end, we analyse potential causal connections between the different sentiment metrics and the price movements.
Leonardo Kanashiro Felizardo, Francisco Caio Lima Paiva, Catharine de Vita Graves, Ălia Yathie Matsumoto ¡ 7 authors
The interdisciplinary relationship between machine learning and financial markets has long been a theme of great interest among both research communities. Recently, reinforcement learning and deep learning methods gained prominence in the active asset trading task, aiming to achieve outstanding performances compared with classical benchmarks, such as the Buy and Hold strategy. This paper explores both the supervised learning and reinforcement learning approaches applied to active asset trading, drawing attention to the benefits of both approaches. This work extends the comparison between the supervised approach and reinforcement learning by using state-of-the-art strategies with both techniques. We propose adopting the ResNet architecture, one of the best deep learning approaches for time series classification, into the ResNet-LSTM actor (RSLSTM-A). We compare RSLSTM-A against classical and recent reinforcement learning techniques, such as recurrent reinforcement learning, deep Q-network, and advantage actorâcritic. We simulated a currency exchange market environment with the price time series of the Bitcoin, Litecoin, Ethereum, Monero, Nxt, and Dash cryptocurrencies to run our tests. We show that our approach achieves better overall performance, confirming that supervised learning can outperform reinforcement learning for trading. We also present a graphic representation of the features extracted from the ResNet neural network to identify which type of characteristics each residual block generates.
Samuel Kwaku Agyei, Anokye M. Adam, Ahmed Bossman, Oliver Asiamah ¡ 7 authors
We present a multi-scale and time-frequency analysis of the degree of integration and the lead-lag relationship between six cryptocurrencies (i.e., Bitcoin, Bitcoincash, Ethereum, Litecoin, Ripple, and Tether) and the cryptocurrency-implied volatility index (VCRIX). As a result, the wavelet techniquesâbi-wavelet, partial wavelet, bivariate contemporary correlations (BCC), wavelet multiple correlations (WMC) and wavelet multiple cross-correlations (WMCC) are applied. Findings from the study provide that the interdependencies between the cryptocurrencies and VCRIX are high and mostly positive across investment horizons. Furthermore, the comovements between the cryptocurrencies designate long memory dynamics. The high comovements between cryptocurrencies are highly influenced by idiosyncratic shocks they possess rather than the VCRIX. In addition, the BCC and the WMC indicate that there is a high integration among all the cryptocurrencies. Categorically, the VCRIX could not lead or lag the interdependencies among the cryptocurrencies in the WMCC analysis. Findings from the study, therefore, divulge that investing in a single or few cryptocurrencies is highly risky due to the adverse impact of the VCRIX on individual cryptocurrencies. In general, investors should effectively hedge against volatilities in the cryptocurrency markets due to the significant predictive ability of VCRIX as an effective proxy.