Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.
Feb 6, 2018·Caginalp, C., & Caginalp, G. (2018). Opinion: Valuation, liquidity price, and stability of cryptocurrencies. Proceedings of the National Academy of Sciences, 115(6), 1131-1134
Cryptocurrencies are examined through the asset flow equations and experimental asset markets. Since tangible value of a typical cryptocurrency is non-existent, the theory suggests that price will gravitate toward liquidity value, i.e., the total amount of cash available for purchase of the asset divided by the number of units. Thus it is unlikely that cryptocurrencies in their current form will be stable in the absence of a mechanism of a link to value.
Elie Bouri, Mahamitra Das, Rangan Gupta, David Roubaud
This paper contributes to the embryonic literature on the relations between Bitcoin and conventional investments by studying return and volatility spillovers between this largest cryptocurrency and four asset classes (equities, stocks, commodities, currencies, and bonds) in bear and bull market conditions. We conducted empirical analyses based on a smooth transition VAR GARCH-in-mean model covering daily data from July 19, 2010 to October 31, 2017. We found significant evidence that Bitcoin returns are related quite closely to those of most of the other assets studies, particularly commodities, and therefore, the Bitcoin market is not isolated completely. The significance and sign of the spillovers exhibited some differences in the two market conditions and in the direction of the spillovers, with greater evidence that Bitcoin receives more volatility than it transmits. Our findings have implications for investors and fund managers who are considering Bitcoin as part of their investment strategies and for policymakers concerned about the vulnerability that Bitcoin represents to the stability of the global financial system.
Hélder Sebastião, António Portugal Duarte, Gabriel Guerreiro
This paper analyses the price discovery in the USD/Bitcoin market since Mar‑2014 to Nov‑2016. The results show a positive relationship between the informational relevance of exchanges and their market shares. Information is mostly transmitted between exchanges within an hour, at least for the main exchanges, although lagged feedbacks occur from the major exchanges. Minor exchanges are merely satellite ones and react to price information with some delay. Bitfinex is the most important exchange: the lagged feedback from this exchange to the market is 18.3%, while the reverse feedback accounts only for 0.6% of the total feedback. Volatility in the major exchanges is the main factor explaining the feedback measures, which sustains the claim that the relative importance of the information-based component of volatility increases with the relative dimension of the exchange.
We present a dynamic Rational Expectations (RE) bubble model of prices with the intention to evaluate it on optimal investment strategies applied to Bitcoin. Our bubble model is defined as a geometric Brownian motion combined with separate crash (and rally) discrete jump distributions associated with positive (and negative) bubbles. The RE condition implies that the excess risk premium of the risky asset exposed to crashes is an increasing function of the amplitude of the expected crash, which itself grows with the bubble mispricing: hence, the larger the bubble price, the larger its subsequent growth rate. We use the RE condition to estimate the real-time crash probability dynamically through an accelerating probability function depending on the increasing expected return. We examine the optimal investment problem in the context of the bubble model by obtaining an analytic expression for maximizing the expected log of wealth (Kelly criterion) for the risky asset and a risk-free asset. Using our bubble model on Bitcoin from 8-Jul-2013 until 19-Dec-2017 would have generated a CAGR of 140% with a maximum drawdown of 69% giving a Calmar Ratio of 2.03. It would have moved out of Bitcoin gradually since 25-Apr-2017 to be completely out on 19-Dec-2017, three days before the crash. The outperformance of the Efficient Portfolio over just investing in Bitcoin was 265%, accomplished over 117 rebalances from 08-Jul- 2013 to 20-Dec-2017. This strategy could thus afford a cost of 2.27% at each rebalancing period and still outperform investing only in Bitcoin.
Cryptocurrency and blockchain has conjointly become trending buzzwords in the business\nworld today. As the blockchain technology has become older and more researched, its areas\nof usage have broadened far beyond payment solutions like Bitcoin. In venture financing,\nblockchain has been used to establish a prominent fundraising tool, called initial coin offerings\n(ICO). An ICO is a crowdfunding method resembling initial public offerings, where ventures\nissue a blockchain based token, subject to public sale. ICO has become a lucrative financing\nmethod for blockchain affiliated ventures.\nThe hype around cryptocurrency has led to increased ICO attention. Everyone can invest in an\nICO, and thus, it has become a popular investment opportunity. This thesis looks at ICOs as\ninvestment objects, with the aim to find out what an investor should consider before investing.\nAdditionally, we assess whether ICOs are profitable financial instruments relative to its close\nsubstitutes, and evaluate measures to avoid scams.\nThe study is based on 104 companies that have had ICOs, and analyzes what factors influence\nboth ICO success rate, and post-ICO capital gains. Our results indicate that hype and pricing\nis influential on the outcome of an ICO, which in turn is important for subsequent price\nmovements. We have also observed that venture capital seed funded companies performed\nbetter in the ICO aftermath. By further using the results, we have also found that investors\nmay use these parameters when investing in an ICO to outperform both our benchmark\ncryptocurrency Ethereum, and other ICOs.
An online social media platform for cryptocurrency investor/traders to share ideas and showcase their portfolios. The platform will be able to capture investment information directly from user's cryptocurrency exchanges account and also factor in their crypto-mining asset to form an interactive portfolios for easy investment statistics tracking or show-casing. The platform will also serve as a advertisement platform for upcoming ICO(Initial Coin Offering),requiring minimal effort to set up and start reaching a targeted audience.
We examine the predictions of the resale option hypothesis (Scheinkman and Xiong, 2003) in cryptocurrency markets. The resale option hypothesis yields testable implications on the relationship between the level and volatility of mispricing, and the degree of heterogeneous beliefs. Using turnover as a proxy for heterogeneity, we find evidence supporting the resale option hypothesis. These findings are persistent across various types of cryptocurrencies, and support the notion that cryptocurrencies trade above intrinsic value. Futhermore, we conduct two backtests to show that portfolios with higher turnover or resale option characteristics underperform portfolios with lower turnover or resale option characteristics. This supports the theory that disagreement is negatively related to future returns for positive biased assets (see Atmaz and Basak, 2018).
With innovation always comes unknowns. Blockchain technology and crypto–assets are no different. Often times, innovators are so worried about getting their product to market or scaling at mass that they overlook the legal ramifications of their innovations. As Mark Zuckerberg infamously said, “move fast and break things.” Facebook was in no way alone in this style of innovation. However, with respect to crypto–assets, the SEC has stepped in and is attempting to prevent the “break things” aspect. One of the major issues relating to crypto–assets is that many people still do not understand what they are, or how the underlying technology works. At the moment, we do not know what to classify crypto– assets as: property, commodities, or something else . If the SEC determines that crypto–assets are investment contracts, the regulation that follows is at risk of putting stranglehold on the underlying innovation and technology. It becomes an issue of balancing consumer protection and innovation for society. SEC v. W.J. Howey Co. laid out a pronged test to determine whether a transaction is an investment contract, subjecting it to securities laws. This note examines the Howey Test to explain why two popular crypto–assets, Bitcoin and Ethereum, are unlikely to satisfy the Howey Test, and briefly addresses the need for clarity in this area. }
This paper studies the efficiency of the cryptocurrency market by looking at the distribution of bitcoin prices over time and across exchange-currency pairs. We document persistent differences in relative bitcoin prices (or discounts), with a half-life of 1 day, and a distribution which is leptokurtic, skewed to the right, with a standard deviation of 3.9%. The variability of discounts is larger in countries with tighter capital controls due to the combined effect of market segmentation and local supply and demand shocks, which we relate to location-specific mining activities and investor attention.
Token sales or initial coin offerings (ICOs) are smart contracts on a blockchain designed to raise external finance by issuing tokens or coins. This introduction provides an overview of this novel financing method. Differences between tokens and coins, types of tokens, and various ICO mechanisms are discussed. The author also describes the evolution of the ICO market and surveys some advantages of ICOs. <b>TOPICS:</b>Currency, volatility measures
Aleš Kozubík University of Žilina – Faculty of Management Science and Informatics – Department of the Mathematical Methods and Operations Research, Univerzitná 8215/1, 010 26 Žilina, Slovak Republic DOI: https://doi.org/10.31410/ITEMA.2018.507 2nd International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2018 – Graz, Austria, November 8, […]