Liquidity Premium, Liquidity-Adjusted Return and Volatility, and Extreme Liquidity

TL;DR

This paper introduces new liquidity premium measures and liquidity-adjusted models for assets with extreme liquidity, demonstrating improved predictability and portfolio performance, especially in volatile crypto markets.

Contribution

It develops innovative liquidity-adjusted ARMA-GARCH/EGARCH models tailored for extreme liquidity assets, with empirical validation in crypto portfolios.

Findings

Models outperform traditional methods in predicting extreme liquidity assets.

Liquidity-adjusted portfolios show better risk-return profiles.

Empirical results support the effectiveness of the new models.

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. We provide empirical support by comparing the performances of a series of Mean Variance portfolios.