0000000000006715
AUTHOR
Niranjan Sapkota
Asset Market Equilibria in Cryptocurrency Markets: Evidence from a Study of Privacy and Non-Privacy Coins
This paper explores whether asset market equilibria in cryptocurrency markets exist. In doing so, it distinguishes between privacy and non-privacy coins. Most recently, privacy coins have attracted increasing attention in the public debate as non-privacy cryptocurrencies, such as Bitcoin, do not satisfy some users’ demands for anonymity. Analyzing ten cryptocurrencies with the highest market capitalization in each sub-market in the 2016–2018 period, we find that privacy coins and non-privacy coins exhibit two distinct market equilibria. Contributing to the current debate on the market efficiency of cryptocurrency markets, our findings provide evidence of market inefficiency. Moreover, the a…
On the Stability of Stablecoins
This paper investigates the volatility processes of stablecoins and their potential stochastic interdependencies with Bitcoin volatility. We employ a novel approach to choose the optimal combination for the power law exponent and the minimum value for the volatilities bending the power law. Our results indicate that Bitcoin volatility is well-behaved in a statistical sense with a finite theoretical variance. Surprisingly, the volatilities of stablecoins are statistically unstable and contemporaneously respond to Bitcoin volatility. Also, whereas the volatilities of stablecoins are not Granger-causal for Bitcoin volatility, lagged Bitcoin volatility exhibits Granger-causal effects on the vol…
Fear Sells: Determinants of Fund-Raising Success in the cross-section of Initial Coin Offerings
This paper explores cross-sectionally the determinants of ICO success as measured by the amount of raised funding. Our study is the first that retrieves an intensive hand-collected data library covering the entire population of all 5,033 ICOs launched in the 2014 – 2019 period. Another important novel aspect is that we address the question whether psychological and financial sentiment cached in whitepapers have an impact on the success of ICOs. We employ natural language procession tools and various sentiment dictionaries to assess the sentiment in whitepapers and our results suggest that ICO investors are largely guided by their emotions when making investment decisions. Additionally, to g…
On the Tail Risk of Cyberattacks in the Bitcoin Market
In the era of digitalization, cryptocurrencies have become an alternative asset for both retail and institutional investors. While the new emerging digital ecosystem based on blockchain technology has been praised for offering plenty of advantages such as decentralization, discretion or increased efficiency in terms of faster settlements among others, investors need to be aware of new types of risks such as hacking incidents. In the 2011-2018 period, about 1.7 million unit of Bitcoin have been stolen corresponding to losses accumulating more than $655 million highlighting the societal impact of this criminal activity. The novel aspect of our study is that it employs a recently proposed appr…
Contagion of Uncertainty: Transmission of Risk from the Cryptocurrency Market to the Foreign Exchange Market
Earlier research documented that cryptocurrencies, including Bitcoin, have experienced dramatic fluctuations in both market capitalization and market share in recent years. Unsurprisingly, Bitcoin returns exhibit higher volatility than traditional G-10 currencies. Our paper extends earlier research and investigates the potential impact of news originating from the Bitcoin market. Confirming earlier studies, we find that Bitcoin exhibits dramatically higher volatility than the dollar factor. Surprisingly, our findings indicate that only hacking incidents that occur in the Bitcoin market result in high levels of co-movement in the risk of both markets the cryptocurrency and the G-10 currency …
Predicting Cryptocurrency Defaults
We examine all available 146 Proof-of-Work based cryptocurrencies that started trading prior to the end of 2014 and track their performance until December 2018. We find that about 60% of those cryptocurrencies were eventually in default. The substantial sums of money involved mean those bankruptcies will have an enormous societal impact. Employing cryptocurrency-specific data, we estimate a model based on linear discriminant analysis to predict such defaults. Our model is capable of explaining 87% of cryptocurrency bankruptcies after only one month of trading and could serve as a screening tool for investors keen to boost overall portfolio performance and avoid investing in unreliable crypt…
Equilibrium in Asset Prices: Evidence from Cryptocurrencies
Employing daily data on ten cryptocurrencies that exhibit the highest market capitalization, we find one instance of cointegration equilibrium in the 2016 2018 period. Contrary to earlier studies that report cryptocurrency markets are developing toward market efficiency, our findings suggest that even the most liquid cryptocurrency markets are inefficient.
How Much Are We Willing To Lose in Cyberspace? On the Tail Risk of Scam in the Market for Initial Coin Offerings
From an entrepreneurial perspective, Initial Coin Offering (ICO) has become an alternative way for attaining funding for business projects using the new evolving digital financial market for tokens. Unfortunately, the majority of all ICOs are subject to scam which casts doubt on this new innovative tool for acquiring funding. Using a unique intensively hand-collected data set covering more than 5000 ICOs which have been launched in the August 2014–December 2019 period, we could identify 1014 ICOs exhibiting data on raised funding whereof 576 turned out to be scams projects. The cumulative losses due to scam in the ICO market correspond to $10.12 billion which is 66% of our identified overal…
On the stability of stablecoins
This paper investigates the volatility processes of stablecoins and their potential stochastic interdependencies with Bitcoin volatility. We employ a novel approach to choose the optimal combination for the power law exponent and the minimum value for the volatilities bending the power law. Our results indicate that Bitcoin volatility is well-behaved in a statistical sense with a finite theoretical variance. Surprisingly, the volatilities of stablecoins are statistically unstable and contemporaneously respond to Bitcoin volatility. Also, whereas the volatilities of stablecoins are not Granger-causal for Bitcoin volatility, lagged Bitcoin volatility exhibits Granger-causal effects on the vol…