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

ArXiv ID: 2407.00813 “View on arXiv”

Authors: Unknown

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.

Keywords: liquidity-adjusted volatility, VECM-DCC/ADCC-Bayesian, multivariate volatility, liquidity jump, market microstructure, Multi-asset (Cryptocurrencies & Stocks)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced econometric techniques like VECM-DCC/ADCC-Bayesian models with posterior covariance estimation, indicating high math complexity. It is heavily data-driven with portfolio optimization and backtesting on cryptocurrency and stock data, showing high empirical rigor.

  flowchart TD
    A["Research Goal"] --> B["Data & Liquidity Measures"]
    B --> C["Liquidity-Adjusted Inputs"]
    C --> D["VECM-DCC/ADCC-Bayesian Estimation"]
    D --> E["Key Findings"]

    subgraph A ["Research Goal"]
        A1["Improve multivariate volatility modeling<br>under market frictions (crypto & stocks)"]
    end

    subgraph B ["Data & Liquidity Measures"]
        B1["Portfolios of Cryptocurrencies & US Stocks"]
        B2["Liquidity Jump (Magnitude)"]
        B3["Liquidity Diffusion (Volatility)"]
    end

    subgraph C ["Liquidity-Adjusted Inputs"]
        C1["Liquidity-Adjusted Returns"]
        C2["Liquidity-Adjusted Volatility"]
    end

    subgraph D ["Computational Process"]
        D1["VECM (Cointegration)"]
        D2["DCC/ADCC (Dynamic Correlation)"]
        D3["Bayesian Inference (Posterior Estimation)"]
    end

    subgraph E ["Key Findings"]
        E1["Traditional models misrepresent volatility & co-movement"]
        E2["Liquidity-adjusted models provide stable & interpretable risk structures"]
        E3["Particularly effective for cryptocurrency portfolios"]
    end