Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.
Bitcoin as well as other cryptocurrencies are all plagued by the impact from bifurcation. Since the marginal cost of bifurcation is theoretically zero, it causes the coin holders to doubt on the existence of the coin's intrinsic value. This paper suggests a normative dual-value theory to assess the fundamental value of Bitcoin. We draw on the experience from the art market, where similar replication problems are prevalent. The idea is to decompose the total value of a cryptocurrency into two parts: one is its art value and the other is its use value. The tradeoff between these two values is also analyzed, which enlightens our proposal of an image coin for Bitcoin so as to elevate its use value without sacrificing its art value. To show the general validity of the dual-value theory, we also apply it to evaluate the prospects of four major cryptocurrencies. We find this framework is helpful for both the investors and the exchanges to examine a new coin's value when it first appears in the market.
This article studies contagion effects between traditional financial markets, represented by five equity indices and the EUR, USD, GBP, and JPY centralized Bitcoin markets. We apply a regime switching skew-normal model of asset returns that distinguishes between linear and non-linear contagion and also structural breaks in the periods. We find significant contagion effects from financial to Bitcoin markets in terms of both correlation and co-skewness of market returns. Our results also indicate that during crisis periods, risk-averse investors tend to move away from risky Bitcoin markets towards safer financial markets.
<h3>Practical Applications Summary</h3> In <b>Cryptocurrency: <i>A New Investment Opportunity?</i></b> from the winter 2018 issue of <b><i>The Journal of Alternative Investments</i></b>, authors <b>David LEE Kuo Chuen</b>, <b>Yu Wang</b> (both of <b>Singapore Management University</b>) and <b>Li Guo</b> (of <b>Singapore Management University</b>) provide an in-depth introduction to Bitcoin and other cryptocurrencies, and explore their potential as an alternative investment class. Their results show that the return correlations between cryptocurrencies and traditional assets are low, and that adding the Cryptocurrency Index (CRIX) to a traditional asset portfolio helps diversify risk, and, under some circumstances, may improve overall performance. Given the difficulty of valuation in the cryptocurrency market, however, the authors explore the possibility of generating positive risk-adjusted profits with a âsentimentâ strategy that evaluates each cryptocurrency individually. <b>TOPICS:</b>Currency, risk management, performance measurement, mutual funds/passive investing/indexing
Purpose The purpose of this paper is to examine the priceâvolume relationship in the bitcoin market to validate near-stock properties of bitcoin. Design/methodology/approach Daily data of bitcoin returns, returns volatility and trading volume (TV) are utilized for the period August 17, 2010âApril 16, 2017. Linear and non-linear causality tests are employed to examine priceâvolume relationship in the bitcoin market. Findings The linear causality analysis indicates that the bitcoin TV cannot be used to predict return; however, the reverse causality is significant. In contrast, the non-linear causality analysis shows that there are non-linear feedbacks between the bitcoin TV and returns. The bitcoin TV, which represents new information, leads to price changes, and large positive price changes lead to increased trading activity. Similarly, in recent periods (post-break period), the results of the non-linear causality test show a unidirectional causality from TV to the volatility of returns. Research limitations/implications This study uses the average index value of major bitcoin exchanges. But further research on this relationship using data from different bitcoin exchanges may provide further insights into the priceâvolume relationship of bitcoin and its near-stock properties. Practical implications These findings from the non-linear causality analysis, therefore, suggest that investors cannot simply base their decisions on the linear dynamics of the bitcoin market. This is because new information in terms of the TV is neither linearly related to the price nor it is a one-to-one kind of relationship as most investors commonly understand it to be. Rather, investorsâ decisions should be based on non-linear models, in general, and the best-fitting non-linear model, in particular. Originality/value The study examines bitcoinâs near-stock properties in a priceâvolume relationship framework with the help of both linear and non-linear causality tests, which to the best of the authorsâ knowledge remains unexplored.
The article aims to bring to light the limits and contradictions of cryptocurrencies, as well as to investigate possible alternative uses of them. Particularly focusing on Bitcoin, understood as a benchmark for the entire sector, the authors seek to answer the following questions: Should Bitcoin be considered a currency, an investment vehicle, or a speculative asset? On which factors does Bitcoin volatility depend? Do Central Banks effectively have no power to influence/stabilize Bitcoin volatility? Following the empirical strategy proposed by Baek and Elbeck, the article shows that Bitcoin returns merely depend on financial conventions and that the cryptocurrency is acting as a highly speculative asset. Sociotechnical innovations introduced by Bitcoin, the authors argue, have concretely opened the possibility of deeply rethinking money. However, several factors are currently negatively affecting the possibility of the cryptocurrency to function as an effective means of payment. Whether this experience can pave the way for the birth of new and more democratic monetary instruments, as the article discusses, is an issue that calls into question a whole combination of political, technical and social elements.
Karunya Rathan, Somarouthu Venkat Sai, T Sai Lakshmi Manikanta
Crypto-currency such as Bitcoin is more popular these days among investors. In the proposed work, it is studied to forecast the Bitcoin price precisely considering different parameters that influence the Bitcoin price. This study first handles, it is identified the price trend on day by day changes in the Bitcoin price while it gives knowledge about Bitcoin price trends. The dataset till current date is taken with open, high, low and close price details of Bitcoin value. Exploiting the dataset machine learning module is introduced for prediction of price values. The aim of this work is to derive the accuracy of Bitcoin prediction using different machine learning algorithm and compare their accuracy. Experiment results are compared for decision tree and regression model.
This paper contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets. By using VAR (Vector Autoregressive Model)-SVAR (Structural Vector Autoregressive Model) Granger causality and Studentâs-t Copulas, we find that Ethereum is likely to be the independent coin in this market, while Bitcoin tends to be the spillover effect recipient. Our study sheds further light on investigating the contagion risks among cryptocurrencies by employing Studentâs-t Copulas for joint distribution. This result suggests that all coins negatively change in terms of extreme value. The investors are advised to pay more attention to âbad newsâ and moving patterns in order to make timely decisions on three types (buy, hold, and sell).
A new market has emerged from active trading of major cryptocurrencies and the listing of many initial coin offering (ICO) tokens. The authors conduct an early investigation into potential factor structures in the expected returns of crypto assets. They find that crypto assets with large market capitalization, low volatility, and high past returns tend to outperform in the following month. These are suggestive evidences for an emerging factor structure, even though crypto asset returns are still largely dominated by idiosyncratic noise. Their findings could help investors make better decisions in the nascent crypto asset market. <b>TOPICS:</b>Currency, exchanges/markets/clearinghouses, quantitative methods
Cryptocurrencies aim at substituting the operations of trusted financial institutions with decentralized operations, including the monetary functions provided by central banks. This article addresses some fundamental questions. Can cryptocurrencies, with no institutions behind them, really function as useful money? Can cryptocurrencies improve our payment system? What are the risks? And how should central banks and other policy makers respond to cryptocurrencies? <b>TOPICS:</b>Currency, portfolio construction, wealth management
This article considers the problem of testing for an explosive bubble in financial data in the presence of time-varying volatility. We propose a sign-based variant of the Phillips, Shi, and Yu (2015, International Economic Review 56, 1043â1077) test. Unlike the original test, the sign-based test does not require bootstrap-type methods to control size in the presence of time-varying volatility. Under a locally explosive alternative, the sign-based test delivers higher power than the original test for many time-varying volatility and bubble specifications. However, since the original test can still outperform the sign-based one for some specifications, we also propose a union of rejections procedure that combines the original and sign-based tests, employing a wild bootstrap to control size. This is shown to capture most of the power available from the better performing of the two tests. We also show how a sign-based statistic can be used to date the bubble start and end points. An empirical illustration using Bitcoin price data is provided.
The main objective of this study is to examine the mutual interaction between crypto money (coins) types. For this purpose, we investigated the sensitivity existence of any crypto money to changes in other crypto types. Methodology-In this study, to find out whether the interaction (relationship) exists between cryptocurrencies VAR model will be used through daily closing prices of each crypt money. Under the VAR analysis, variance decomposition, impact-response functions analysis will be done and finally, Granger Causality Test will be performed. Findings-According to the results of VAR analysis based on Variance Decomposition, BITCOIN, BT CASH and Tether are largely external variables and their prices are not significantly affected by other crypto currencies. In contrast, the values of Etherum, Lite Coin and QTUM are significantly affected by the changes in the values of other crypto coins. Conclusion-In accordance with findings obtained from analysis, we observed that Tether is moving towards becoming an alternative investment tool for all the crypto moneys. Other crypto coins tend to move in the same direction.
This paper develops the ability of the normal inverse Gaussian distribution (NIG) to fit the returns of bitcoin (BTC). As the first cryptocurrency created, the behavior of this new asset is characterized by great volatility. The lack of a proper definition or classification under existing theory exacerbates this property in such a way that explosive periods followed by a rapid decline have been observed along the series, meaning bubble episodes. By detecting the periods in which a bubble rises and collapses, it is possible to study the statistical properties of such segments. In particular, adjusting a theoretical distribution may help to determine better strategies to hedge against these episodes. The NIG is an appropriate candidate not only because of its heavy-tailed property but also because it has been proven to be closed under convolution, a characteristic that can be implemented to measure multivariate value at risk. Using data on the price of BTC with respect to seven of the main global currencies, the NIG was able to fit every time segment despite the bubble behavior. In the out-of-sample tests, the NIG was proven to have an adjustment similar to that of a generalized hyperbolic (GH) distribution. This result could serve as a starting point for future studies regarding the statistical properties of cryptocurrencies as well as their multivariate distributions.