Daniel Traian Pele, Miruna Mazurencu-Marinescu-Pele
In this paper, the authors investigate the statistical properties of some cryptocurrencies by using three layers of analysis: alpha-stable distributions, Metcalfe’s law and the bubble behaviour through the LPPL modelling. The results show, in the medium to long-run, the validity of Metcalfe's law (the value of a network is proportional to the square of the number of connected users of the system) for the evaluation of cryptocurrencies; however, in the short-run, the validity of Metcalfe’s law for Bitcoin is questionable. As the results showed a potential for herding behaviour, the authors then used LPPL models to capture the behaviour of cryptocurrencies exchange rates during an endogenous bubble and to predict the most probable time of the regime switching. The main conclusion is that Metcalfe’s law may be valid in the long-run, however in the short-run, on various data regimes, its validity is highly debatable.
<p>This paper is aimed at studying a MS-GARCH model applied to Bitcoin. The Bayesian estimation of the model shows that Bitcoin’s volatility can be modelled using two states of volatility, high and low. The modelled volatility is not stable over time. Twenty eight periods of high volatility were found, the largest period of volatility occurred during 2013. The findings help explain what happened during these high volatility periods.</p><p> </p><p><strong> </strong></p>
Mike Cudd, Kristen Ritterbush, Marcelo Eduardo, Chris Smith
One of the most significant innovations in the world of finance has been the creation and evolvement of cryptocurrencies. These digital means of exchange have been the focus of extensive news coverage, especially the Bitcoin, with a primary focus on the tremendous potential return and the high level of accompanying risk. In this chapter, we examine the risk-return pattern for an array of cryptocurrencies, contrasting the pattern with those of conventional currency and equity investments. We find the measures of cryptocurrency returns and risk to be a very high multiple of those of conventional investments, and the pattern is determined to be robust relative to the time frame. Consequently, cryptocurrencies are determined to provide an alternative to investors that involves tremendously high risk and return.
The goal of this chapter is to present recent developments about Bitcoin1 price modeling and related applications. Precisely, we consider a bivariate model in continuous time to describe the behavior of Bitcoin price and of the investors’ attention on the overall network. The attention index affects Bitcoin price through a suitable dependence on the drift and diffusion coefficients and a possible correlation between the sources of randomness represented by the driving Brownian motions. The model is fitted on historical data of Bitcoin prices, by considering the total trading volume and the Google Search Volume Index as proxies for the attention measure. Moreover, a closed formula is computed for European-style derivatives on Bitcoin. Finally, we discuss two possible extensions of the model. Precisely, we investigate the relation between the correlation parameter and possible bubble effects in the asset price; further, we consider a multivariate framework to represent the special feature of Bitcoin being traded on several exchanges and we discuss conditions to rule out arbitrage opportunities in this setting.
Matthias Schnaubelt, Jonas Rende, Christopher Krauß
The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on the literature, we establish a structured methodological framework to conduct analyses in a systematic and comprehensive way. We then present results from a unique and extensive limit order data set acquired from major cryptocurrency exchanges for the currency pair Bitcoin to US Dollar. We recover many observations from mature markets, such as a symmetry between the average ask and the average bid side of the order book, autocorrelation in returns on the smallest time scales only, volatility clustering and the timing of large trades. We also observe some idiosyncrasies: The distributions of trade size and limit order prices deviate from commonly observed patterns. Also, we find limit order books to be relatively shallow and liquidity costs to be relatively high when compared to established markets.
Abstract Using extreme value analysis, we investigate the tail risk behavior of the high‐frequency (hourly) log returns of four most popular cryptocurrencies. The analysis is conducted on high‐frequency returns data, estimating value at risk and expected shortfall with varying thresholds. We find that Ripple is the most risky cryptocurrency exhibiting the largest potential gain or loss for both positive and negative (hourly) log returns at every percentile and threshold. Bitcoin is the least risky cryptocurrency.
Recent research on the economics of digitization investigates the dramatic changes in markets by digital technology. Digital technology has caused significant differences in in the cost of storage, computation, and transmission of data. As one of the latest sign of digitization in our life, the use of cryptocurrencies has been drawing attention of all market players. Blockchain technology is recently reallocating resources, restructuring of routines, changing market relationships and patterns of the flow of goods and services. This study investigates the coherence of Bitcoin with the movements of the main indicators of different markets. Aim of this research is to clarify whether this cryptocurrency follows the market conditions or not. Because the movements of the market values of Bitcoin are aimed to be investigated to show that they can be used separately as a tool of risk management. For this reason, wavelet analysis has been employed to define cross-correlation between time series of the daily USD value of Bitcoin and some market indicators. Some literature asserts that Bitcoin has recently started to follow market conditions. If Bitcoin process follows the market conditions more than before, that if This analysis will explain if there is a change in hedging possibility of Bitcoin recently.
Daniel Traian Pele, Miruna Mazurencu-Marinescu-Pele
In this paper we investigate the ability of several econometrical models to forecast value at risk for a sample of daily time series of cryptocurrency returns. Using high frequency data for Bitcoin, we estimate the entropy of intraday distribution of logreturns through the symbolic time series analysis (STSA), producing low-resolution data from high-resolution data. Our results show that entropy has a strong explanatory power for the quantiles of the distribution of the daily returns. Based on Christoffersen's tests for Value at Risk (VaR) backtesting, we can conclude that the VaR forecast build upon the entropy of intraday returns is the best, compared to the forecasts provided by the classical GARCH models.
We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading.
The cryptocurrency market is a very huge market without effective\nsupervision. It is of great importance for investors and regulators to\nrecognize whether there are market manipulation and its manipulation patterns.\nThis paper proposes an approach to mine the transaction networks of exchanges\nfor answering this question.By taking the leaked transaction history of Mt. Gox\nBitcoin exchange as a sample,we first divide the accounts into three categories\naccording to its characteristic and then construct the transaction history into\nthree graphs. Many observations and findings are obtained via analyzing the\nconstructed graphs. To evaluate the influence of the accounts' transaction\nbehavior on the Bitcoin exchange price,the graphs are reconstructed into series\nand reshaped as matrices. By using singular value decomposition (SVD) on the\nmatrices, we identify many base networks which have a great correlation with\nthe price fluctuation. When further analyzing the most important accounts in\nthe base networks, plenty of market manipulation patterns are found. According\nto these findings, we conclude that there was serious market manipulation in\nMt. Gox exchange and the cryptocurrency market must strengthen the supervision.\n
The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this question. By taking the leaked transaction history of Mt. Gox Bitcoin exchange as a sample, we first divide the accounts into three categories according to its characteristic and then construct the transaction history into three graphs. Many observations and findings are obtained via analyzing the constructed graphs. To evaluate the influence of the accounts' transaction behavior on the Bitcoin exchange price, the graphs are reconstructed into series and reshaped as matrices. By using singular value decomposition (SVD) on the matrices, we identify many base networks which have a great correlation with the price fluctuation. When further analyzing the most important accounts in the base networks, plenty of market manipulation patterns are found. According to these findings, we conclude that there was serious market manipulation in Mt. Gox exchange and the cryptocurrency market must strengthen the supervision.
OlaOluwa S. Yaya, Ahamuefula E. Ogbonna, Robert Mudida, Nuruddeen Abu
Abstract This article investigates both market efficiency and volatility persistence in 12 cryptocurrencies during pre‐crash and post‐crash periods. The article contributes to the debate on the market efficiency of cryptocurrencies in the presence of volatility, considering robust fractional integration methods in both linear and nonlinear setups. We find that markets of Bitcoin and most altcoins considered in our study can be dubbed as efficient, and are also highly volatile, particularly, in the post‐crash period that we are experiencing now. The volatilities are more likely to persist for a shorter period than volatilities in the pre‐crash period. Our work, therefore, renders important information to cryptocurrency market participants and portfolio managers.
Muhammad Ali Nasir, Toan Luu Duc Huynh, Sang Phu Nguyen, Duy Duong
In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a VAR framework, a copulas approach, and non-parametric drawings, to capture a dependence structure. Using a weekly dataset from 2013 to 2017, our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume. Shocks to search values have a positive effect, which persisted for at least a week. Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics, which are of special interest to investors and economic policymakers.
Purpose This study aims to compare investors of major conventional currencies and Bitcoin (BTC) investors by using the value at risk (VaR) method common risk measure. Design/methodology/approach The paper used a risk analysis named as VaR. The analysis has various computations that Historical Simulation and Monte Carlo Simulation methods were used for this paper. Findings Findings of the analysis are assessed in two different aspects of singular currency risk and portfolios built. First, BTC is found to be significantly risky with respect to the major currencies; and it is six times riskier than the singular most risky currency. Second, in terms of inclusion of BTC into a portfolio, which equally weights all currencies, it elevates overall portfolio risk by 98 per cent. Practical implications In spite of the remarkable risk level, it could be considered that investors are desirous of making an investment on BTC could mitigate their overall exposed risk relatively by building a portfolio. Originality/value The paper questions the risk level of Bitcoin, which is a digital currency. BTC, a matter of debate in the contemporary period, is seen as a digital currency free from control or supervision of a regulatory board. With the comparison of major currencies and BTC shows that how could be risky of a financial instrument without regulations. However, there is some advice for investors who would like to invest digital currencies despite the risk level in this study.