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

Paper: arXiv 2306.15807

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.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric models (ARMA-GARCH/EGARCH) and derives liquidity-adjusted return/volatility measures, indicating high mathematical complexity. It demonstrates empirical rigor by using tick-level crypto data, comparing portfolio performances, and addressing wash trades, though it lacks the reproducibility markers of full backtesting code or datasets.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Model Assets with Extreme Liquidity<br>& Improve Predictability"] --> B{"Data: Crypto Asset Portfolio"}
  B --> C["Methodology: Liquidity-Adjusted<br>ARMA-GARCH/EGARCH Models"]
  C --> D{"Computation: Liquidity Premium,<br>Adjusted Return & Volatility"}
  D --> E["Performance Comparison:<br>Mean-Variance Portfolios"]
  E --> F["Key Finding: Liquidity-Adjusted<br>Models Offer Superior Predictability"]