Purpose In this paper, the authors seek to investigate the dynamics of Bitcoin, Litecoin, Ethereum and Ripple daily returns and volatilities. Design/methodology/approach In this paper, the authors apply the MS-ARMA model on daily returns of Bitcoin (19/04/2013-13/02/2018), Ripple (05/08/2013-14/02/2018), Litcoin (29/04/2013-14/02/2018) and Ethereum (08/02/2015-14/02/2018). This model allows capture of the nonlinear structure in both the conditional mean and the conditional variance of cryptocurrency returns. Findings All the cryptocurrency markets show regime switching in the return-generating process. Market dynamics seem to be governed by two different states which differ from one cryptocurrency market to another in terms of mean return, volatility and interstate dynamics. These findings can be explained by investorsâ behavior, i.e. speculative trading and herding behavior. By choosing to participate (or imitating some investors) in some cryptocurrency markets (in particular Bitcoin market), they affect the price movements and therefore the market dynamics in the short run. Practical implications Identifying the different market states provides information for investors to make more accurate portfolio decisions in the virtual market and follow the market timing strategy. Originality/value This paper attempts to analyze potential nonlinear structure in cryptocurrencies returns and analyze if there is a difference between the cryptocurrencies market cycles. So, the search for congruent and adequate specification to reproduce the stock returns dynamics in the virtual market still remains the concern of several empirical studies. This research not only examines the behavior of stock returns in the cryptocurrenciesâ market but also highlights the existence of nonlinearity propriety as a stylized fact.
Purpose Bitcoin has recently become the focal point of investors as a digital currency and an alternative payment method. Despite Bitcoin being in the spotlight, a gap in the literature on its price-setting behaviors has been observed. This study aims to contribute to the literature by investigating the relationship between Bitcoin price and volume in the period between January 1, 2012 and April 7, 2018 through a symmetric and asymmetric causality test. Design/methodology/approach Daily price and volume data relevant to Bitcoin traded in the Bitstamp market were obtained from www.bitcoincharts.com . Within the framework of data applicable for analysis, the data set for this study includes a total of 2,286 observations for the period between January 1, 2012 and April 7, 2018. Findings Based on the results of the standard causality test, a causality relationship was determined from price to volume. Based on the results of the asymmetric causality test between positive and negative shocks of variables, a unilateral causality relationship was determined from negative shocks in Bitcoin prices to negative shocks in trading volume as well as from positive shocks in trading volume to positive shocks in prices. Furthermore, it was found that the relationship between Bitcoin price and volume is cointegrated. Practical implications The empirical results can be used by investors and portfolio managers to make trading decisions. Originality/value The contribution of this paper to the literature is that it is the first study on the symmetric and asymmetric causality relationship between Bitcoin price and volume. Moreover, this paper reveals short- and long-term behaviors of Bitcoin using the cointegration test used for determining the long-term relationship between Bitcoin price and volume.
Blockchain networks and similar cryptoeconomic networks are systems, specifically complex systems. They are adaptive networks with multiscale spatio-temporal dynamics. Individual actions may be incentivized towards a collective goal with âpurpose-drivenâ tokens. Blockchain networks, for example, are equipped cryptoeconomic mechanisms that allow the decentralized network to simultaneously maintain a universal state layer, support peer-to-peer settlement, and incentivize collective action. These networks represent an institutional infrastructure upon which socioeconomic collaboration is facilitated â in the absence of intermediaries or traditional organizations. They provide a mission-critical and safety-critical regulatory infrastructure for autonomous agents in untrusted economic networks. Their tokens provide a rich, real-time data set reflecting all economic activities in their systems. Advances in network science and data science can thus be leveraged to design and analyze these economic systems in a manner consistent with the best practices of modern systems engineering. Research that reflects all aspects of these socioeconomic networks needs (i) a complex systems approach, (ii) interdisciplinary research, and (iii) a combination of economic and engineering methods, here referred to as âeconomic systems engineering,â for the regulation and control of these socioeconomic systems. This manuscript provides a conceptual framework synthesizing the research space and proceeds to outline specific research questions and methodologies for future research in this field, applying an inductive approach based on interdisciplinary literature review and relative contextualization of the works cited.
Robert Mundell (1961 Mundell, R. A. 1961. âA Theory of Optimum Currency Areas.â The American Economic Review 51 (4): 657â665.[Web of Science Âź] , [Google Scholar]. âA Theory of Optimum Currency Areas.â The American Economic Review 51 (4): 657â665) framed the fixed-versus-flexible exchange rate controversy in a novel way when he focused attention on currency areas and the ingredients necessary for a group of nations to form an optimal currency area (OCA). This paper proposes an analogous theory for cryptocurrencies, called optimal currency scope (OCS), and explains the conditions necessary for an OCS to exist. In contrast to an OCA, which is defined by its non-overlapping geographic areas, an OCS is defined by its multiple-overlapping attributes and the needs they solve, which we call scope. Scopes are not geographic and have fuzzy boundaries; so, the possibility of competing currencies in a single scope needs to be considered. The central issues surrounding an OCS are the optimal number and magnitude of currency attributes, rather than whether nations should adopt fixed versus fluctuating exchange rates. Similar to the findings since Mundell first introduced his OCA Theory, we find that the optimum number of cryptocurrency attributes and, therefore, the optimal number of cryptocurrencies must be determined empirically rather than theoretically.
Sung Min Jang, Eojin Yi, Woo Chang Kim, Kwangwon Ahn
This paper studies the causal relationship between Bitcoin and other investment assets. We first test Granger causality and then calculate transfer entropy as an information-theoretic approach. Unlike the Granger causality test, we discover that transfer entropy clearly identifies causal interdependency between Bitcoin and other assets, including gold, stocks, and the U.S. dollar. However, for symbolic transfer entropy, the dynamic riseâfall pattern in return series shows an asymmetric information flow from other assets to Bitcoin. Our results imply that the Bitcoin market actively interacts with major asset markets, and its long-term equilibrium, as a nascent market, gradually synchronizes with that of other investment assets.
This paper provides a systematic survey on return and volatility spillovers of cryptocurrencies based on the empirical results of relevant academic literature. Evidence reveals that Bitcoin is the most influential among digital coins mainly as a transmitter toward digital currencies but also as a receiver of spillovers from virtual currencies and alternative assets. Ethereum, Litecoin, and Ripple present the most significant interlinkages with Bitcoin. Return spillovers are more pronounced but volatility spillovers often present a bi-directional character. Volatility shock transmission is detected among Bitcoin and national currencies, while economic policy uncertainty is not influential. This survey provides useful guidance in the hotly-debated issue of reform and decentralization of financial systems.
One of the crucial cornerstone in cryptocurrency is the ability to generate random sequences and aleatory numbers with a null chance that any eavesdropper could extract information about the one-to-one transactions. Under the assumption that the Quantum Mechanics transition probability is perceived as a fully blockchain operation, random sequences are generated as a tool to protect transactions entirely based on Bitcoins. We test the algorithm with integer-order Bessel functions inside of a full blockchain-based algorithm. Simulations have demonstrated the reliability of a simple model of blockchain in the order of 75.
The goal of this study is to analyze the predictive power of selected financial variables over principal cryptocurrencies (Bitcoin, Ethereum, and Ripple) by applying the PGARCH, EGARCH, TGARCH, and GARCH models. The studied variables include daily Google trend values of the selected cryptocurrencies' names considered as search terms; the daily log-returns of the exchange rate of major currencies per SDRs; and the daily log-returns of the S&P500 index, gold, and oil prices. Our results provide evidence that the log-returns of each studied cryptocurrency have significant explanatory over each other. We also find significant results for the daily Google trend values of the search terms âBitcoinâ and âRipple,â but not for âEthereum.â Likewise, we find significant results for the log-returns of the exchange rate of the Chinese Yuan per SDRs for the three cryptocurrencies. Similarly, the log-returns of oil prices have a significant relationship with Bitcoin and Ethereum, but not with Ripple. Correspondingly, we find that the daily log-returns of the S&P500 index has explanatory power for all selected cryptocurrencies; however, the daily log-returns of gold have no significant relationship with any of them. Finally, we find that the log-returns of oil have a significant relationship with Bitcoin only. Regarding the volatility of the studied cryptocurrencies, our results provide mixed evidence about the presence of leverage effects.
Van Minh Hao, Nguyen Huynh Huy, Bo Dao, Thanh-Tan Mai · 5 authors
Predicting cryptocurrency price movements is a challenging task due to the highly stochastic nature of the market. This paper exploits features from social media, combining with the historical price to build an accurate model for predicting trending of the Bitcoin, the most popular cryptocurrency these days. The novelty of this work is introducing a new feature called 'interaction' which helps improve the model's performance significantly. Our approach shows a very promising result, which outperforms other recent works by a large margin.
In this paper, we apply Long Short-Term Memory (LSTM) neural networks to model the token price time series data which incorporate the local topological measures of investor transaction network and market summaries. In addition, we propose a novel LSTM-based model using the leave-one-out cross-validation technique and utilizing the network motif analysis. The numerical results show that the proposed LSTM-based model could significantly improve the performance of the prediction of token price compared to the benchmark LSTM-based models and deep portfolios regardless of the training and testing data split ratio. Some concluding remarks and future research directions are provided.
The study of connectedness is key to assess spillover effects and identify lead-lag relationships among market exchanges trading the same asset. By means of an extension of Diebold and Yilmaz (2012) econometric connectedness measures, we examined the relationships of five major Bitcoin exchange platforms during two periods of main interest: the 2017 surge in prices and the 2018 decline. We concluded that Bitfinex and Gemini are leading exchanges in terms of return spillover transmission during the analyzed time-frame, while Bittrexs act as a follower. We also found that connectedness of overall returns fell substantially right before the Bitcoin price hype, whereas it leveled out during the period the down market period. We confirmed that the results are robust with regards to the modeling strategies.
This study examines the effect of US monetary growth on Bitcoin trading volume. To achieve this purpose, firstly, the symmetric causality test is used. Following this test, another symmetric causality test is used to reveal a time-varying causal effect between variables. The data set covers the period from July 2010 to July 2019. The results of the first symmetric causality test, which considers the time interval of the study data as a whole, show that there is no causal relationship between variables. According to the results of the second causality test, these support the previous results substantially. However, an interesting detail is the causal relationship between variables for the period between April 2019 and July 2019. The reason for this relationship could be that investors who are indecisive during the current economic uncertainty add Bitcoin to their portfolios in response to the Federal Reserve's decisions.
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15th May 2013. Transitions from one class to another with time are related to the maturement of cryptocurrencies. In speculative cryptocurrency markets, these findings have potential implications for investment and trading strategies.
As crypto-currencies hold dual nature of a medium of exchange (currency) and an investment asset, some questions may arise about the potentiality of including crypto-currencies as liquid investment asset in financial institutions particularly in the banking sector to enhance their liquidity risk management and improve their portfolio diversification investment strategy. The objective of this study therefore is to examine the characteristics of Bitcoin currency based on the requirements of High-Quality Liquid Assets (HQLA) standards of Basel III and compare its volatility structure with other traditional asset classes that are already recommended by Basle III as HQLA. The study utilizes both descriptive and quantitative analysis using the GARCH family models to examine the volatility structures of these assets. The findings show that Bitcoin currency holds the same characteristics of HQLA, however; the risk of legality and recognition is still under consideration by legal authorities around the world and this risk will be eradicated in the future as crypto-currencies derive their legality from their real intrinsic value, multi-economic usefulness and not by law as in the case of fiat money currency. Furthermore, the symmetric volatility structure analysis shows the continuing persistence of volatility and predictability behavior in return series of Bitcoin currency and other- traditional asset classes in the U.S. market. However, Bitcoinâs stability has gradually improved over time. With regard to the asymmetric informative response, Bitcoin returns respond more to negative shock but it has no statistical significance, thus suggesting the lack of leveraging effect in Bitcoin market but this effect was found to be statistically persistent in other traditional asset class markets. In addition, Bitcoin returns show very low correlation with other traditional asset classes. All these imply that Bitcoin is a potential candidate as a hedge and asset diversifier, which is recommended to be included in the HQLA. This study provides some support to recent theoretical work on crypto asset return behaviour and liquidity risk management. The findings provide appropriate information about Bitcoin asset behaviour compared to other traditional asset classes which will enable them to make the right investment decision with regard to hedging, diversification and liquidity risk management. The findings of this study may assist in evaluating the suitability of including crypto assets into HQLA to improve the liquidity requirement standards and ensure that banks have an adequate amount of HQLA specifically during times of financial turmoil.
This paper explores empirically the behavior of the Chicago Mercantile Exchange (CME) bitcoin futures contract. The analysis focuses on the time period between the launch of the CME bitcoin futures contract on December 18, 2017, and September 17, 2018. The behavior of the bitcoin spot market and CME futures market is compared and analyzed along several dimensions: price, volatility and liquidity. By comparing the Garman-Klass volatilities of bitcoin spot and futures prices with those of different assets, we find that both the bitcoin spot and futures markets exhibit relatively high volatility compared to other assets. When the ratio of trading volume over open interest is used to measure liquidity, the bitcoin futures market shows a mid-level liquidity. We also find while the exchange margin is set to meet the normal price volatility that can cover the daily price movements within one standard deviation, the brokerage margin for bitcoin futures is set at beyond two standard deviations. Some brokerage firms impose non-margin requirements such as high net account balance and open position limits in addition to regular margins. We conclude that the brokerage firms' relatively high margin and non-margin requirements impede trading activity such as short-sales and thus, liquidity and efficiency in the bitcoin futures market has been slow to develop.
Purpose The development of blockchain and cryptocurrency may alleviate the economic strain associated with recession. Economic recessions tend to be aggregate-demand driven, meaning that they are caused by fluctuations in the supply of or demand for money. Holding monetary policy as solution assumes that stability must arise from outside of the economic system. Under a policy regime that allows innovations in blockchain to develop, blockchain technology may promote a money supply that is responsive to changes in demand to hold money. The purpose of this paper is to suggest that cryptocurrencies present an opportunity to profitably implement rules that promote macroeconomic stability. In particular, cryptocurrency that is asset-backed may provide a means for cheaply attaining liquidity during a crisis. Design/methodology/approach The role of cryptocurrency in promoting macroeconomic equilibrium is approached through the lens of monetary theory. Moves away from macroeconomic equilibrium necessitate either a change in the average price of money or a change in the quantity of money, or a change in portfolio demand for money. Cryptocurrency promotes an increase, however this requires the alignment of policy regulating the use of cryptocurrency, reduction in taxes placed on the use of cryptocurrency and cryptocurrency protocol. Findings Cryptocurrency is unlikely to become legal tender, but it may alleviate macroeconomic fluctuations as a near money that provides liquidity and whose supply is sensitive to changes in demand to hold money and money-like substitutes. This role might be inhibited if policy stifles the development of cryptocurrencies and blockchain technology. Research limitations/implications New financial innovations like cryptocurrencies can be analyzed applying the equation of exchange in light of the mechanics of money creation under conditions of disequilibrium. Monetary disequilibrium may be promoted by policy that causes bottlenecks in financial markets. Originality/value Theory of monetary disequilibrium has broad implications for the development and regulation of financial markets. This theory has not been applied to the development of cryptocurrency markets.
We study the Bitcoin and Ether price series under a financial perspective. Specifically, we use two econometric models to perform a two-layer analysis to study the correlation and prediction of Bitcoin and Ether price series with traditional assets. In the first part of this study, we model the probability of positive returns via a Bayesian logistic model. Even though the fitting performance of the logistic model is poor, we find that traditional assets can explain some of the variability of the price returns. Along with the fact that standard models fail to capture the statistic and econometric attributesâsuch as extreme variability and heteroskedasticityâof cryptocurrencies, this motivates us to apply a novel Non-Homogeneous Hidden Markov model to these series. In particular, we model Bitcoin and Ether prices via the non-homogeneous PĂłlya-Gamma Hidden Markov (NHPG) model, since it has been shown that it outperforms its counterparts in conventional financial data. The transition probabilities of the underlying hidden process are modeled via a logistic link whereas the observed series follow a mixture of normal regressions conditionally on the hidden process. Our results show that the NHPG algorithm has good in-sample performance and captures the heteroskedasticity of both series. It identifies frequent changes between the two states of the underlying Markov process. In what constitutes the most important implication of our study, we show that there exist linear correlations between the covariates and the ETH and BTC series. However, only the ETH series are affected non-linearly by a subset of the accounted covariates. Finally, we conclude that the large number of significant predictors along with the weak degree of predictability performance of the algorithm back up earlier findings that cryptocurrencies are unlike any other financial assets and predicting the cryptocurrency price series is still a challenging task. These findings can be useful to investors, policy makers, traders for portfolio allocation, risk management and trading strategies.
Faruk Balli, Anne de Bruin, Md Iftekhar Hasan Chowdhury, Muhammad Abubakr Naeem
We investigate dynamic connectedness of cryptocurrencies using Generalized Forecast Error Variance Decomposition into short, medium, and long-term phases. Though all phases resemble similar drifts, the magnitude of short-term connectedness is much higher than medium and long-term. Notably, the U-shaped trajectory explains the increasing connectedness of cryptocurrencies with their heightened popularity in recent times. We further utilize Continuous Wavelet Transform to rationalize such connectedness with common economic and financial market uncertainty. Importantly, we show that as economic uncertainty increases, the connectedness among cryptocurrencies decreases. This highlights the potential for cryptocurrencies to be an alternative instrument for hedging against underlying uncertainty.