Liquidity Adjustment in Multivariate Volatility Modeling: Evidence from Portfolios of Cryptocurrencies and US Stocks

TL;DR

This paper introduces a liquidity-sensitive multivariate volatility framework that improves covariance estimation by incorporating novel liquidity measures, leading to more stable risk assessments especially for cryptocurrency portfolios.

Contribution

It develops two new portfolio-level liquidity measures and integrates them into a Bayesian multivariate volatility model, enhancing risk estimation under market frictions.

Findings

Liquidity-adjusted models provide more stable risk estimates.

Traditional models misrepresent volatility during liquidity stress.

Cryptocurrency portfolios benefit from liquidity-aware risk modeling.

Abstract

We develop a liquidity-sensitive multivariate volatility framework to improve the estimation of time-varying covariance structures under market frictions. We introduce two novel portfolio-level liquidity measures, liquidity jump and liquidity diffusion, which capture magnitude and volatility of liquidity fluctuation, respectively, and construct liquidity-adjusted return and volatility that reflect real-time liquidity variability. These liquidity-adjusted inputs are integrated into a VECM-DCC/ADCC-Bayesian model, allowing for conditional and posterior covariance estimation under liquidity stress. Applying this framework to portfolios of cryptocurrencies and US stocks, we find that traditional models misrepresent volatility and co-movement, while liquidity-adjusted models yield more stable and interpretable risk structures, particularly for portfolios of cryptocurrencies. The findings support the use of liquidity-adjusted multivariate models as statistically grounded tools for assessing the propagation of portfolio risk under market frictions, with implications for asset pricing, market microstructure design, and portfolio management.