Using the generalized extreme value theory to characterize tail distributions, we address liquidation, leverage and optimal margins for bitcoin long and short futures positions. The empirical analysis of perpetual bitcoin futures on BitMEX shows that (1) daily forced liquidations to outstanding futures are substantial at 3.51% and 1.89% for long and short; (2) investors got forced liquidation do trade aggressively with average leverage of 60X; and (3) exchanges should elevate current 1% margin requirement to 33% (3X leverage) for long and 20% (5X leverage) for short to reduce the daily margin call probability to 1%. Our results further suggest that normality assumption on return significantly underestimates optimal margins. Policy implications are also discussed.
The cryptocurrency market is emerging as a new asset class for the investment. As the traditional asset prices are often noted to be influenced by the liquidity risk, this study links the cryptocurrency liquidity cost to its yields. Pre-pandemic uncertainty, the Bitcoin liquidity cost was found to be priced in its returns during the same trading session. Post-pandemic crisis, the relationship was changed. The liquidity cost was reported not to be priced in the Bitcoin returns at the time of same trading session. Post-pandemic crisis, however, the liquidity cost imposed by the liquidity supplier on day t â 1 was noted to be priced in the Bitcoin return of day t . In the cryptocurrency market, this study quantifies the effects on the Bitcoin returns of its liquidity cost, and if such effects vary pre- and post-pandemic uncertainty.
Cryptocurrency prices differ across countries, and these price deviations fluctuate widely. Our paper provides evidence that distrust toward domestic authorities can explain the dynamics of local cryptocurrency prices relative to the U.S. dollar price. The price deviation rises after an outbreak of a financial crisis, political scandal, or socioeconomic event that undermines confidence in the domestic government or economy. With panel regressions, we show that Bitcoin price deviations increase by 1.8% when the institutional failure index rises by one standard deviation. These price responses are much stronger in countries with lower trust levels and during periods with tighter capital controls.
The cryptocurrency market recently gained a lot of attention from investors. But, its volatility has been acting as a disincentive to investment. Volatility plays an important role in shaping market riskiness and investment behavior. We study the volatility of the Ethereum (ETH) cryptocurrency from the following perspectives. The first goal of this study is to identify risk-seeking behavior in the ETH cryptocurrency market. We examine this propensity by measuring the effect of the volatility of Ethereum on the total ETH assets. This investigation also takes the form of a case-study of an unexpected ETH fund-stolen event, DAO Hack, and the hard fork treatment. We also forecast a downward volatility trend in the near future based on Autoregressive models. This is the first study to analyze DAO Hack with empirical methods and marks the starting point for more rigorous models to predict the volatility of Ethereum.
Yizhou Cao, Min Dai, Steven Kou, Lewei Li ¡ 5 authors
Abstract Existing cryptocurrencies are too volatile to be used as currencies for daily payments. Stablecoins, which are cryptocurrencies pegged to other stable financial assets such as the US dollar, are desirable for payments within blockchain networks, whereby being often called the âHoly Grail of cryptocurrency.â By using the option pricing theory and the Ethereum platform that allows running smart contracts, we design several dualâclass structures that are written on the ETH cryptocurrency and offer a fixedâincome crypto asset (Class A coin), a stablecoin (Class AⲠcoin) pegged to a traditional currency, and leveraged investment instruments (Class B and BⲠcoins). Our investigation of the values of stablecoins in the presence of jump risk and black swanâtype events shows the robustness of the design. The design has been implemented on the Ethereum platform.
Abstract Several cryptocurrency (CC) indices track the dynamics of the rising CC sector, and soon ETFs will be issued on them. We conduct a qualitative and quantitative evaluation of the currently existing CC indices. As the CC sector is not yet consolidated, index issuers face the challenge of tracking the dynamics of a fast-growing sector that is under continuous transformation. We propose several criteria and various measures to compare the indices under review. Major differences between the indices lie in their weighting schemes, their coverage of CCs and the number of constituents, the level of transparency, and thus, their accuracy in mapping the dynamics of the CC sector. Our analysis reveals that simple market cap-weighted indices outperform their competitors. Interestingly, increasing the number of constituents does not automatically lead to a better fit of the CC sector. All codes are available on "Image missing".
We investigate the dynamics of daily realised returns and risk premiums for a large cross-section of cryptocurrency pairs through the lens of an Instrumented Principal Component Analysis (IPCA) (see Kelly et al., 2019). We show that a model with three latent factors and time-varying factor loadings significantly outperforms a benchmark model with observable risk factors: the total (predictive) R2 from the IPCA is 17.2% (2.9%) for individual returns, against a benchmark 9.6% (-0.02%) obtained from a model with six observable risk factors explored in previous literature. By looking at the characteristics that significantly matter for the dynamics of risk premiums, we provide robust evidence that liquidity, size, reversal, and both market and downside risks represent the main driving factors behind expected returns. These results hold for both individual assets and characteristic-based portfolios, pre and post the Covid-19 outbreak, and for weekly individual and portfolio returns.
Purpose Motivated by the lure of cryptocurrencies for retail investors, whose concentrated holdings are particularly exposed to price crash risk, this paper aims to study the relationship between investor attention and crash risk for a range of cryptocurrencies. Design/methodology/approach This study adopts a quantile regression approach to determine the effect of investor attention on crash risk. Crash risk is measured using the negative coefficient of skewness and down up volatility. Findings This study finds that the connection is concentrated in the tails of the crash risk distribution. Investor attention has a positive relationship with crash risk when crash risk is low (below-median quantiles) and negative when crash risk is high (above-median). The findings are consistent for different measures of crash risk, for alternate internet searches and for a panel of large cryptocurrencies in addition to Bitcoin. This study also notes seasonality in crash risk, with higher crash risk during the JuneâAugust period and lower crash risk in the Halloween period that runs from November to April. Originality/value The results provide insights that are not apparent in previous analyses of cryptocurrency price crash risk. The results are particularly important for retail investors, who constitute a large portion of the cryptocurrency market, as they tend to hold concentrated investments and so a price crash of a single asset may have a large bearing on their wealth.
This study investigates how uncertainty surrounding cryptocurrency affects cryptocurrency return (CR) by employing various wavelet techniques. To this end, we concentrate on the recently published cryptocurrency uncertainty index (UCRY) and the top eight cryptocurrencies by virtue of market capitalization for the period from December 30, 2013, until February 21, 2021. Our results show that the UCRY index strongly predicts CR. In particular, the UCRY index has a leading position in all the frequencies for all cryptocurrencies in our sample. Additionally, when the impacts of economic policy uncertainty and the volatility index are eliminated, the significant co-movement of UCRY-CR stays unchanged for short-, medium-, and long-term investment horizons. Thus, we conclude that the UCRY-CR relationships are both persistent and pervasive. Our study contributes to the literature on the relationships between cryptocurrency and market uncertainties as well as to investors who use uncertainty indices to design their investment strategies for their portfolios.
This article considers a variety of highly diversified cross-sectional momentum and reversal strategies, with sorting and holding periods from one week up to two years. In a sample of the 2,000 largest cryptocurrencies during the period 2014â2020, we identify positive momentum on short horizons up to two to four weeks and a significant reversal on longer horizons beyond one month. The reversal effect becomes more pronounced once we expand the sorting and/or holding periods. Momentum and, particularly, reversal returns are economically large, statistically significant, and generally not exposed to standard cryptocurrency risk factors. The main drivers of the reversal effect are âpast loserâ cryptocurrencies. The switching of momentum into reversal occurs after approximately one monthâmuch quicker than the equity market, and evidence of the âfaster metabolism of cryptocurrencies.â