Abstract In this paper, cryptocurrencies are analysed as investment instruments. The study aims to verify whether they can be classified as an asset class and what kind of benefits they may bring to the investor's portfolio. We used 6 indices as proxies for the major asset classes, including the cryptocurrency index CRIX, for all cryptographic assets. Cryptocurrencies relatively fully satisfied 7 asset class requirements, namely stable aggregation, investability, internal homogeneity, external heterogeneity, expected utility, selection skill and cost-effective access. It was found that crypto assets have diversification properties. Portfolio optimisation with the Modern Portfolio Theory showed an increase in the Sharpe ratio of tangency portfolios with the inclusion of CRIX. However, the Post-Modern Portfolio Theory identified significant deterioration of the downside risk and the Sortino ratio.
Ambreen Khursheed, Muhammad Abubakr Naeem, Sheraz Ahmed, Faisal Mustafa
This study examines the adaptive market hypothesis (AMH) in relation to time-varying market efficiency by using three tests, namely Generalized Spectral (GS), Dominguez-Lobato (DL) and the automatic portmanteau test (AP) test on four-digital currencies; Bitcoin, Monaro, Litecoin, and Steller over the sample period of 2014–2018. The study applies Jarque-Bera test, ADF test, Ljung-Box statistics and ARCH-LM test for testing normality of returns, stationarity of series, serial correlation and volatility clustering in returns and squared returns of selected cryptocurrencies. Further, the study adopts an extremely important category of martingale difference hypothesis (MDH), which uses non-linear methods of dependencies for identifying changing linear and non-linear dependence in the price movement of currencies. The results indicate that price movements with linear and nonlinear dependences varies over time. Our tests also reveal that Bitcoin, Monaro and Litecoin have the longest efficiency periods. While Steller shows the longest inefficient market period. In view of varying market conditions, the results indicate that different market periods have significant impact on prices fluctuations of cryptocurrencies. Therefore, our findings suggest implementing the adaptive market hypothesis (AMH) as predicting changes in cryptocurrency prices over time must consider the time-varying market conditions for efficient forecasting.
María Belén Arouxét, Aurelio F. Bariviera, Verónica Pastor, Victoria Vampa
Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into a complex ecosystem of high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of seven important coins. Our study covers the pre-Covid-19 and the subsequent pandemic period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.
The underlying transparency of the Bitcoin blockchain allows transactions in the network to be tracked in near real-time. When someone transfers a large number of Bitcoins, the market receives this information and traders can adjust their expectations based on the new information. This paper investigates trading volume and its relation to asymmetric information around transfers on the Bitcoin blockchain. We collect data on 2132 large transactions on the Bitcoin blockchain between September 2018 and November 2019, where 500 or more Bitcoins were transferred. Using event study methodology, we identify significant positive abnormal trading volume for the 15-minute window before a large Bitcoin transaction as well as during and after the event. Using public information about Bitcoin addresses of cryptocurrency exchanges as proxies for information asymmetry, we find that transactions with high levels of information asymmetry negatively affect abnormal trading volume once the event becomes public knowledge, while some effects are even opposite for transactions with lower information asymmetry. The results show that blockchain transaction activity is a relevant aspect of Bitcoinns microstructure, as informed traders make use of the information in general and adjust their expectations based on the degree of information asymmetry.
Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks, called 'primitives,' for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers (or CFMMs) which includes existing popular market makers such as Uniswap, Balancer, and Curve, whose yearly transaction volume totals to billions of dollars. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset and that they can do so in a computationally efficient way. We also derive several other useful properties that were previously not known. These include lower bounds on the total value of assets held by CFMMs and lower bounds guaranteeing that no agent can, by any set of trades, drain the reserves of assets held by a given CFMM.
This paper documents a persistent structure in cryptocurrency returns and analyzes a broad set of characteristics that explain this structure. The results show that similarities in size, trading volume, age, consensus mechanism, and token industries drive the structure of cryptocurrency returns. But the highest variation is explained by a "connectivity" measure that proxies for similarity in cryptocurrencies' investor bases using their trading location. Currencies connected to other currencies that perform well generate sizably higher returns than the cross-section both contemporaneously and in the future. I examine three potential channels for these results. First, evidence from new exchange listings and a quasi-natural experiment shows that unobservable characteristics cannot explain the effect of connectivity. Second, decomposition of the order flows suggests that connectivity captures strong exchange-specific commonalities in crypto investors' demand that also spills over to other exchanges. Finally, analysis of social media data suggests that these demand shocks are a first order driver of cryptocurrency returns, largely because they can be perceived as a sign of user adoption.
One of the most important innovations brought by digitalization is crypto money known as virtual money. Cryptocurrencies, which have been discussed in recent years and especially a new portfolio for investors, are very popular. Bitcoin is the most well-known of these cryptographic systems, which do not depend on a central authority and have maximum reliability. The effects of various financial indicators on cryptoparas were examined in this study. The model includes a daily database in between April 3, 2018 to December 31, 2019. Initially stationarity is tested with unit root tests. Then cointegration and causality tests are employed. Impulse response is also implemented and analysed.
Abstract This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010–17 and 2010–19. Price discovery is achieved through the Gonzalo–Granger permanent‐transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018–19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.
Abstract By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.
Tommy Crépellière, Matthias Pelster, Stefan Zeisberger
Arbitrage opportunities in markets for cryptocurrencies are well-documented. In this paper, we confirm that they existed; however, their magnitude decreased greatly from April 2018 onward. Analyzing various trading strategies, we show that it is hardly possible to exploit existing price differences since then. We discuss and test several mechanisms that may be responsible for the increased market efficiency and find that, particularly, informed trading is correlated with a reduction in arbitrage opportunities.
This paper compares mathematical models for automated market makers including logarithmic market scoring rule (LMSR), liquidity sensitive LMSR (LS-LMSR), constant product/mean/sum, and others. It is shown that though LMSR may not be a good model for Decentralized Finance (DeFi) applications, LS-LMSR has several advantages over constant product/mean based automated market makers. However, LS-LMSR requires complicated computation (i.e., logarithm and exponentiation) and the cost function curve is concave. In certain DeFi applications, it is preferred to have computationally efficient cost functions with convex curves to conform with the principle of supply and demand. This paper proposes and analyzes constant circle/ellipse based cost functions for automated market makers. The proposed cost functions are computationally efficient (only requires multiplication and square root calculation) and have several advantages over widely deployed constant product cost functions. For example, the proposed market makers are more robust against front-runner (slippage) attacks.
We investigate the puzzle of widespread participation in cryptocurrency pump-and-dump manipulation schemes. Unlike stock market manipulators, cryptocurrency manipulators openly declare their intentions to pump specific coins, rather than trying to deceive investors. Puzzlingly, people join in despite negative expected returns. In a simple framework, we demonstrate how overconfidence and gambling preferences can explain participation in these schemes. Analyzing a sample of 355 cases in 6 months, we find strong empirical support for both mechanisms. Pumps generate extreme price distortions of 65% on average, abnormal trading volumes in the millions of dollars, and large wealth transfers between participants.
Pairs trading is a strategy based on exploiting mean reversion in prices of securities. Even though these strategies have been shown to perform well for equities, their performance is unknown for the field of cryptocurrencies, usually perceived as inefficient and predictable. We apply the distance and cointegration methods to a basket of 26 liquid cryptocurrencies traded on the Binance exchange, specifically at 5-minute, 1-hour and daily frequencies. In our backtests, the strategies underperform classical benchmarks. However, the results are quite sensitive to parameter settings and external factors such as transaction costs or execution windows. Higher-frequency trading delivers significantly better performance, and while the most common daily distance method returns -0.07% monthly, this increases to 11.61% monthly for 5-minute frequency. Additionally, we find evidence of simple mean-reverting behavior in intraday prices that is missing in daily data, and which provides further support for the inefficiency of cryptocurrency markets.
This paper analyses herding in cryptocurrency markets in the time of the COVID-19 pandemic. We employ a combination of quantitative methods to hourly prices of the four most traded cryptocurrency markets - USD, EUR, JPY and KRW - for the period from 1st January 2019 to 13th March 2020. While there are several strong theoretical reasons to observe the “black swan” effect on cryptocurrency herding, our results suggest that COVID-19 does not amplify herding in cryptocurrency markets. In all markets studied, herding remains contingent on up or down markets days, but does not get stronger during the COVID-19. These results are important for cryptocurrency investors and regulators to enhance their understanding of cryptocurrency markets and the financial effects of the COVID-19 pandemic.
ABSTRACT The meteoric growth of global cryptocurrency markets presents novel challenges to regulators. Some policymakers and scholars warn that regulation will cause trading activity to cross borders into less-regulated jurisdictions—or even smother a promising new financial asset class. Others believe regulatory actions will stimulate activity by providing clarity to market participants. Standing behind this disagreement is a debate about the desirability of either outcome. Some believe that governments should promote development of the cryptocurrency sector within their countries, while others view cryptocurrencies as conduits of illegality and fraud that should be restricted through strict regulation or even outright bans. Yet these debates have, to date, been conducted almost entirely without data concerning the effects of regulation on market activity. As a corrective, in this article we assembled original data on cryptocurrency regulations worldwide and used them to empirically examine movement in trading activity at a number of exchanges following key regulatory announcements. We found that a wide variety of models yielded almost entirely null results. From the creation of bespoke licensing regimes to targeted anti-money-laundering and anti-fraud enforcement actions, as well as many other categories of government activities, we found no systemic evidence that regulatory measures cause traders to flee, or enter into, the affected jurisdictions. These findings at last provide an empirical basis for regulatory decisions concerning cryptocurrency trading. Among other things, they call into question that capital flight or chilling effects should be a first-order concern.
This is the first paper that explores lottery-like demand in cryptocurrency markets. Since recent research provides evidence that cryptocurrency returns appear to be short-memory processes, we modify Bali, Cakici and Whitelaw’s (2011) and Bali, Brown, Murray, and Tang’s (2017) MAX measure and employ a weekly forecast horizon and daily log-returns from the previous week to calculate the metric for our portfolio sorts. From an econometric point of view, this study proposes statistical tests that are robust to unknown dynamic dependency structures in the cryptocurrency data. Our results show that average raw and risk-adjusted return differences between cryptocurrencies in the lowest and highest MAX quintiles exceed 1.50% per week. These results are robust after controlling for Bitcoin risk or potential microstructure effects. Our findings are important also from a theoretical point of view because they suggest that parallel to stock markets, similar behavioral mechanisms of underlying investor behavior are present also in new virtual currency markets.