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Cryptoasset Factor Models

Last Updated on 10 February, 2024 by Rejaul Karim

In “Cryptoasset Factor Models,” Zura Kakushadze presents an innovative framework for understanding the cross-section of daily returns in the realm of cryptoassets. Encompassing cryptocurrencies and a diverse array of digital assets, the study introduces factor models designed to capture the intricacies of cryptoasset returns and offers accessible source code for data retrieval, risk factor computation, and out-of-sample backtesting.

Through empirical analysis, the research identifies a dominant factor significantly influencing daily cryptoasset returns, signaling the potential for cross-sectional statistical arbitrage trading in cryptoassets, contingent upon efficient executions and shorting.

With a comprehensive lens on factors including size, volume, momentum, and volatility, the study not only delves into the intricacies of cryptoasset returns but also lays the groundwork for actionable insights and strategic applications in this dynamic and evolving landscape.

Abstract Of Paper

We propose factor models for the cross-section of daily cryptoasset returns and provide source code for data downloads, computing risk factors and backtesting them out-of-sample. In “cryptoassets” we include all cryptocurrencies and a host of various other digital assets (coins and tokens) for which exchange market data is available. Based on our empirical analysis, we identify the leading factor that appears to strongly contribute into daily cryptoasset returns. Our results suggest that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting.

Original paper – Download PDF

Here you can download the PDF and original paper of Cryptoasset Factor Models.

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Author

Zura Kakushadze
Quantigic Solutions LLC; Free University of Tbilisi

Conclusion

In conclusion, “Cryptoasset Factor Models” by Zura Kakushadze offers a pioneering framework for understanding and harnessing the dynamics of daily cryptoasset returns. Through the introduction of factor models and accessible source code for data retrieval and risk factor computation, the study equips researchers and practitioners with valuable tools for analyzing and backtesting cryptoasset returns in an out-of-sample context.

The identification of a dominant factor exerting a strong influence on daily cryptoasset returns presents compelling implications for potential cross-sectional statistical arbitrage trading, contingent upon efficient trade executions and shorting strategies.

With an in-depth exploration of factors such as size, volume, momentum, and volatility, the research not only sheds light on the intricacies of cryptoasset returns but also offers actionable insights for leveraging these factors in the burgeoning cryptoasset market.

Related Reading:

Currency Carry Trades, Position-Unwinding Risk, and Sovereign Credit Premia

Momentum Effects in the Cryptocurrency Market after One-Day Abnormal Returns

FAQ

Q1: What is the main focus of the research paper “Cryptoasset Factor Models” by Zura Kakushadze?

A1: The main focus of the research paper is to propose factor models for the cross-section of daily returns in the realm of cryptoassets. The term “cryptoassets” encompasses not only cryptocurrencies but also various other digital assets, including coins and tokens, for which exchange market data is available. The study aims to provide a comprehensive framework for understanding and analyzing the dynamics of daily returns in the cryptoasset market.

Q2: What does the research paper offer in terms of practical tools for researchers and practitioners?

A2: The research paper provides valuable practical tools for researchers and practitioners in the form of source code. The source code is designed for data downloads, computing risk factors, and conducting out-of-sample backtesting. By offering accessible source code, the study empowers individuals to retrieve data, calculate risk factors, and test their models in real-world scenarios, facilitating hands-on analysis and experimentation with cryptoasset returns.

Q3: What is the scope of “cryptoassets” considered in the research, and why is it significant?

A3: The term “cryptoassets” in the research includes all cryptocurrencies as well as various other digital assets such as coins and tokens, provided that exchange market data is available for them. This broad scope is significant because it acknowledges the diverse array of digital assets in the cryptocurrency ecosystem. By considering a wide range of cryptoassets, the research aims to capture the complexity and heterogeneity of the market, providing a more comprehensive understanding of factors influencing daily returns.

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