Crypto Research Weekly 2

There are many interesting working paper this week. The main topic of this week is around "risk".

"Criptocrashes" 
ALEKSEY KOLOKOLOV, University of Manchester - Manchester Business School
Email: Alexeiuo@gmail.com

This paper proposes a new nonparametric test for detecting short-lived locally explosive trends (drift bursts) in pure-jump processes. The new test is designed specifically to detect intraday flash crashes and gradual jumps in cryptocurrency prices recorded at a high frequency. Empirical analysis shows that drift bursts in bitcoin price occur, on average, every second day. Their economic importance is highlighted by showing that hedge funds holding cryptocurrency in their portfolios are exposed to a risk factor associated with the intensity of bitcoin crashes. On average, hedge funds do not profit from intraday bitcoin crashes and do not hedge against the associated risk.

Comment: The finding that drift bursts in bitcoin prices occur approximately every second day underscores the volatility and unpredictability inherent in cryptocurrency markets. Furthermore, the paper's demonstration of the economic importance of these drift bursts, particularly in relation to the exposure of hedge funds holding cryptocurrencies, sheds light on the risks associated with such investments. The revelation that, on average, hedge funds neither profit from nor hedge against intraday bitcoin crashes suggests potential shortcomings in their risk management strategies. This underscores the need for more sophisticated risk management techniques tailored to the unique dynamics of cryptocurrency markets.


"Measuring Defi Risk" 
JEREMY BERTOMEU, Washington University in St. Louis - John M. Olin Business School
Email: bjeremy@wustl.edu
XIUMIN MARTIN, Washington University in Saint Louis - Olin School of Business
Email: Xmartin@wustl.edu
IBRAHIMA SALL, Washington University in St. Louis
Email: i.sall@wustl.edu

Decentralized finance (DeFi) use cryptocurrencies as collateral, matching speculative margin traders with yield-seeking depositors who lend coins pegged to the dollar (stable coins). We develop a risk framework that requires only knowledge of total deposits and borrowings to measure total systemic risk to lenders and borrowers. Using data from major protocols, system fragility increased beyond reasonable levels around mid-2021, and a potential collateral loss occurs with extreme fluctuations in coin prices. Moreover, our results indicate that the liquidation process, a risk mitigation strategy, is not timely and is less effective for high-risk levels.

Comment: The observation that system fragility increased significantly around mid-2021 underscores the inherent volatility and challenges associated with DeFi platforms. This vulnerability exposes participants to potential collateral losses during extreme fluctuations in coin prices, emphasizing the need for robust risk management protocols within DeFi ecosystems. The finding that the liquidation process, a key risk mitigation strategy, is not timely and is less effective for high-risk levels is concerning. Timely and effective liquidation mechanisms are essential for maintaining the stability and integrity of DeFi platforms, especially during periods of heightened market volatility.


"Asymmetric Reverting and Volatility in Cryptocurrency Markets" 
JUSTICE KYEI-MENSAH, affiliation not provided to SSRN

The study employed the asymmetric nonlinear smooth transition generalized autoregressiveconditional heteroskedasticity (ANST-GARCH) model to investigate the asymmetric responses of mean reversion and volatility.in cryptocurrency markets. The data set is a daily observation period spanning from 20th April 2013 to 21st February 2023. The study found strong evidence for the ANST-GARCH model as well as considerable asymmetries concerning the behaviour of investors who overreact to certain cryptocurrency market news. The study also revealed that asymmetric volatility is crucially significant in cryptocurrency markets and that investors can implement risk management techniques and trading strategies to mitigate the effect.

Comment: The findings of the study suggest strong empirical support for the ANST-GARCH model, indicating its suitability for capturing the complexities inherent in cryptocurrency market dynamics. The identification of considerable asymmetries in investor behavior, particularly in response to cryptocurrency market news, underscores the presence of distinct patterns of overreaction among market participants. Moreover, the study's revelation of the significance of asymmetric volatility in cryptocurrency markets highlights the importance of understanding and managing risk effectively in these highly volatile and dynamic environments. By recognizing and accounting for asymmetries in volatility, investors can implement more robust risk management techniques and trading strategies to mitigate potential adverse effects and capitalize on market opportunities.

"Price, Trade Volume and Return in the Ethereum Market" 
OSAMA LIAQAT, affiliation not provided to SSRN
Email: osama.60032@iqra.edu.pk
KEHKASHAN NIZAM, Iqra University
Email: Kehkashan.60003@iqra.edu.pk
DR. JAHANZAIB ALVI, affiliation not provided to SSRN
Email: jahanzaib.52655@iqra.edu.pk

This research focuses on analyzing the price-setting behaviors of Ethereum, a digital currency and alternative payment system that has gained significant attention from investors. By employing symmetric and asymmetric causality tests, the study investigates the relationship between Ethereum's return, price, and trading volume from August 7, 2015, to November 30, 2020. The data, obtained from CoinMarketCap.com, consists of 1,940 observations suitable for analysis. The research employs ARDL, ARDL bound tests, and VECM to achieve its objectives. The findings indicate a significant and positive association between Ethereum's return, price, and trading volume in both the short and long run.

Comment: The findings of the research reveal a significant and positive association between Ethereum's return, price, and trading volume both in the short and long run. This suggests that changes in Ethereum's return and trading volume have a noticeable impact on its price dynamics over time, affirming the interdependence and interconnectedness of these variables within the Ethereum ecosystem. By employing rigorous econometric techniques and a substantial dataset spanning several years, the study contributes valuable insights into the dynamics of Ethereum's market and sheds light on the factors influencing its price-setting mechanisms. These findings have implications for investors, analysts, and policymakers seeking to better understand and navigate the cryptocurrency landscape, particularly in the context of Ethereum's role and behavior as a digital asset and payment system.