Liquidity Premium, Liquidity-Adjusted Return and Volatility, and Extreme Liquidity
Develops liquidity-adjusted ARMA-GARCH models for crypto assets, demonstrating improved predictability and portfolio performance under extreme liquidity.
What it examines
This paper develops a framework to model assets with extreme liquidity, focusing on crypto assets. It introduces liquidity-adjusted return and volatility measures and proposes liquidity-adjusted ARMA-GARCH/EGARCH models to improve predictability under extreme liquidity conditions.
What it concludes
The study confirms that liquidity-adjusted models enhance predictability for assets with extreme liquidity, offering a robust alternative to traditional models. Potential applications include better portfolio optimization and risk management for assets with high liquidity risk.
Evidence objects
The study confirms that liquidity-adjusted models enhance predictability for assets with extreme liquidity, offering a robust alternative to traditional models. Potential applications include better portfolio optimization and risk management for assets with high liquidity risk.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: We establish innovative liquidity premium measures, and construct liquidity-adjusted return and volatility to model assets with extreme liquidity, represented by a portfolio of selected crypto assets, and upon which we develop a set of liquidity-adjusted ARMA-GARCH/EGARCH models. We demonstrate that these models produce superior predictability at extreme liquidity to their traditional counterparts… ▽ More We establish innovative liquidity premium measures, and construct liquidity-adjusted return and volatility to model assets with extreme liquidity, represented by a portfolio of selected crypto assets, and upon which we develop a set of liquidity-adjusted ARMA-GARCH/EGARCH models. We demonstrate that these models produce superior predictability at extreme liquidity to their traditional counterparts. We provide empirical support by comparing the performances of a series of Mean Variance portfolios. △ Less
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