Liquidity Jump

Liquidity Jump, Liquidity Diffusion, and Crypto Wash Trading ArXiv ID: 2411.05803 “View on arXiv” Authors: Unknown Abstract We develop a new framework to detect wash trading in crypto assets through real-time liquidity fluctuation. We propose that short-term price jumps in crypto assets results from wash trading-induced liquidity fluctuation, and construct two complementary liquidity measures, liquidity jump (size of fluctuation) and liquidity diffusion (volatility of fluctuation), to capture the behavioral signature of wash trading. Using US stocks as a benchmark, we demonstrate that joint elevation in both liquidity metrics indicates wash trading in crypto assets. A simulated regulatory treatment that removes likely wash trades confirms this dynamic: it reduces liquidity diffusion significantly while leaving liquidity jump largely unaffected. These findings align with a theoretical model in which manipulative traders amplify both the level and variance of price pressure, whereas passive investors affect only the level. Our model offers practical tools for investors to assess market quality and for regulators to monitor manipulation risk on crypto exchanges without oversight. ...

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. ...