We consider the hedging problem where a futures position can be automatically\nliquidated by the exchange without notice. We derive a semi-closed form for an\noptimal hedging strategy with dual objectives - to minimise both the variance\nof the hedged portfolio and the probability of liquidations due to insufficient\ncollateral. The optimal solution depends on the statistical characteristics of\nthe spot and futures extreme returns and parameters that characterise the\nhedger by loss aversion, choice of leverage and collateral management. An\nempirical analysis of bitcoin shows that the optimal strategy combines superior\nhedge effectiveness with a reduction in the probability of liquidation. We\ncompare the performance of seven major direct and inverse hedging instruments\ntraded on five different exchanges, based on minute-level data. We also link\nthis performance to novel speculative trading metrics, which differ markedly\nbetween venues.\n
This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories, extremes, and show greater persistence in anomalies over time.
Abstract This paper examines how liquidity risk is priced in the cross‐section of cryptocurrency returns. In doing so, we use the Amihud measure as a liquidity proxy. By employing the univariate portfolio analysis, the bivariate portfolio analysis, and the Fama‐MacBeth regression analysis, we document a negative relationship between liquidity and cryptocurrency returns. Additional tests demonstrate that this finding is robust to alternative liquidity measurement as well as size screens and show no evidence of a significant intertemporal relationship between liquidity and expected returns for three leading cryptocurrencies. Our conclusions add to the understanding of how markets price cryptocurrencies.
Cryptocurrencies are attracting more investors and are reaching higher prices than ever, hence it becomes important to analyse whether the new asset class is a bubble or not. Previous literature on examination of cryptocurrency bubbles has primarily focused on Bitcoin, but the newer cryptocurrencies such as Ethereum are innovating the space with smart contracts, upstaging Bitcoin on some aspects, hence it becomes important to analyse the newer cryptocurrencies apart from Bitcoin as well for rational bubbles. The methods of recursive unit root tests suggested by Phillips, Shi and Yu (2015) has been used in this study to check for the presence of bubbles and to date stamp the periods of exuberance in three major cryptocurrencies: Bitcoin, Ethereum and Ripple from 2016 to 2021. Similarity in exuberance periods of Bitcoin and Ethereum is also detected in this research.
Real-time financial settlements constrain traders to have the cash on hand before they can enter a trade [Khapko and Zoican, 2017]. This prevents short-selling and ultimately impedes liquidity. We propose a novel trading protocol which relaxes the cash constraint, and manages chains of deferred payments. Traders can buy without paying first, and can re-sell while still withholding payments. Trades naturally arrange in chains which contract when deals are closed and extend when new ones open. Default risk is handled by reversing trades. In this short note we propose a class of novel financial instruments for zero-risk and zero-collateral intermediation. The central idea is that bilateral trades can be chained into trade lines. The ownership of an underlying asset becomes distributed among traders with positions in the trade line. The trading protocol determines who ends up owning that asset and the overall payoffs of the participants. Counterparty risk is avoided because the asset itself serves as a collateral for the entire chain of trades. The protocol can be readily implemented as a smart contract on a blockchain. Additional examples, proofs, protocol variants, and game-theoretic properties related to the order-sensitivity of the games defined by trade lines can be found in the extended version of this note [Danos et al., 2019]. Therein, one can also find the definition and game-theoretic analysis of standard trade-lines with applications to trust-less zero-collateral intermediation.
In this study, it was investigated whether the Covid-19 pandemic, which started to affect the world in early 2020, influenced the relationship between return volatility and trading volume in the cryptocurrency market. In the empirical part of the study, 40 cryptocurrencies were included in the analysis. The data were divided into two separate periods as before and during the pandemic. Two alternative estimators developed by Garman and Klass (1980) and by Rogers and Satchell (1991) were used to measure the return volatility of cryptocurrencies. With causality and simultaneous correlation analyses, it was determined that the sequential information arrival hypothesis was valid in the cryptocurrency market in the pre-pandemic period. In the pandemic period, the sequential information arrival hypothesis lost its effect and left its place to the mixture of distribution hypothesis.
Cryptocurrency works on a system that admits people to make payments all over the world without the requirement for any intermediary. Most digital currencies experience frequent periods of intense volatility. This paper examines the day of the week effects in return and volatility on Bitcoin, Ethereum, Ripple, Litecoin, and Tether currencies. To estimate volatile variance, this research uses five ARCH family models: ARCH, GARCH, EGARCH, TARCH and PARCH Models. The best models are derived based on Akaike Info Criterion and Schwarz Criterion. The sample periods vary based on the date of the initial release of each currency up to 31 December 2019. Results indicate the Power ARCH (PARCH) is the best model for Bitcoin and Litecoin, Threshold ARCH (TARCH) model is the best for Ethereum, Ripple, and Litecoin, and the EGARCH model is for Tether. Each model shows a different day of the week effects on each currency.
Weihao Han, David Newton, Emmanouil Platanakis, Charles Sutcliffe · 5 authors
Cryptocurrency returns are highly non-normal, casting doubt on the standard performance metrics. We apply almost stochastic dominance (ASD), which does not require any assumption about the return distribution or degree of risk aversion. From 29 long-short cryptocurrency factor portfolios, we find eight that dominate our four benchmarks. Their returns cannot be fully explained by the three-factor coin model of Liu et al. (2022). So we develop a new three-factor model where momentum is replaced by a mispricing factor based on size and risk-adjusted momentum, which significantly improves pricing performance.
On January 4, 2021 the Office of the Comptroller of the Currency (OCC), a major regulator of financial institutions in the United States, announced that federally chartered banks and thrifts were now allowed to utilize stablecoins as payment instruments. Much research and many discussions have revolved around policies of governments worldwide in how to handle the new cryptocurrency phenomenon. The purpose of this short study was to observe the valuation impact of that announcement on the three largest cryptocurrencies and two others. Research findings show the altcoins with valuations not tied to the dollar had substantial increases in value while the stablecoins, which the announcement specified are now allowed to be used by banks, changed very little. Specifically, Bitcoin and Etherium increased over 20% in value within 5 days of the announcement while the stablecoins Tether and USDCoin changed in value by no more than 0.10% for the same event window. This shows that stablecoins lived up to their name even though they were promoted as an acceptable payment system in the US.
This dissertation is dedicated to the analysis of three superordinate economic principles in varying market environments: market efficiency, the behavior of market participants and information asymmetry. Sustainability and social responsibility have gained importance as investment criteria in recent years. However, responsible investing can lead to conflicting goals with respect to utility-maximizing behavior and portfolio diversification in efficient markets. Conducting a meta-analysis, this thesis presents evidence that positive (non-monetary) side effects of responsible investing can overcome this burden. Next, the impact of the EU-wide regulation of investment research on the interplay between information asymmetry, idiosyncratic risk, liquidity and the role of financial analysts in stock markets is investigated. An empirical analysis of the emerging primary and secondary market for cryptocurrencies yields further insights about the effects of information asymmetry between investors, issuers and traders. The efficient allocation of resources is dependent on the market microstructure, the behavior of market participants, as well as exogenous shocks. Against this background, this thesis is dedicated to the empirical analysis of limit order books, the rationality of traders and the impact of COVID-19. Due to its young history, the market for cryptocurrencies yields a suitable research subject to test classical financial theories. This doctoral thesis reveals parallels between the microstructure of cryptocurrency and stock markets and uncovers some previously unknown statistical properties of the cryptocurrency market microstructure. An initial examination of the impact of COVID-19 further shows that cryptocurrencies with a high market capitalization seem to react to macroeconomic shocks similar to stock markets. This cumulative dissertation comprises six stand-alone papers, of which three papers have already been published.
This study examines several asset pricing specifications applied for a sample of 72 cryptocurrencies. We extend the existing literature on asset pricing of cryptocurrencies by including higher co-moment factors, namely co-skewness and co-kurtosis. Our overall conclusion is that co-skewness and co-kurtosis are also priced in crypto-markets, but less pronouncedly than in equity/commodity/derivatives markets. Size and momentum factors further increase explanatory power, but their regression coefficients are insignificant.
Research background: Since the financial crisis in 2008, numerous other cryptocurrencies have established themselves in the financial industry alongside Bitcoin. Although the validity of the user cases is still lacking, Bitcoin is already being used extensively in the institutional finance sector, among others. Here, the comparison of Bitcoin to other asset classes in mixed portfolio structures must be taken into account. According to the latter, far-reaching areas of investigation emerge by adding Bitcoin in the evaluation of risk-return ratios of mixed portfolio weightings. Purpose of the article: The objective of this paper is to examine, within the framework of Harry Markowitz’s efficiency theory, the impact of including Bitcoin as an investment asset for the risk-return ratios of mixed portfolio structures. Methods: The statistical analysis is based, among other things, on paired sample tests, where the return and volatility values are tested for significant differences in the selected test values. Findings & Value added: The statistical investigations show that the introduction of Bitcoin leads to advantageous return structures, but at the same time to significantly increased volatility values of the examined portfolio constellations. Setting a regional focus of the investment assets in the investigations led to a simplified evaluation basis and at the same time offers the scientific space for further investigations.