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on Market Microstructure |
| By: | Vladim\'ir Hol\'y |
| Abstract: | We address the challenges of modeling high-frequency integer price changes in financial markets using continuous distributions, particularly the Student's t-distribution. We demonstrate that traditional GARCH models, which rely on continuous distributions, are ill-suited for high-frequency data due to the discreteness of price changes. We propose a modification to the maximum likelihood estimation procedure that accounts for the discrete nature of observations while still using continuous distributions. Our approach involves modeling the log-likelihood in terms of intervals corresponding to the rounding of continuous price changes to the nearest integer. The findings highlight the importance of adjusting for discreteness in volatility analysis and provide a framework for incroporating any continuous distribution for modeling high-frequency prices. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.09785 |
| By: | Haochuan (Kevin); Wang |
| Abstract: | Liquidity withdrawal is a critical indicator of market fragility. In this project, I test a framework for forecasting liquidity withdrawal at the individual-stock level, ranging from less liquid stocks to highly liquid large-cap tickers, and evaluate the relative performance of competing model classes in predicting short-horizon order book stress. We introduce the Liquidity Withdrawal Index (LWI) -- defined as the ratio of order cancellations to the sum of standing depth and new additions at the best quotes -- as a bounded, interpretable measure of transient liquidity removal. Using Nasdaq market-by-order (MBO) data, we compare a spectrum of approaches: linear benchmarks (AR, HAR), and non-linear tree ensembles (XGBoost), across horizons ranging from 250\, ms to 5\, s. Beyond predictive accuracy, our results provide insights into order placement and cancellation dynamics, identify regimes where linear versus non-linear signals dominate, and highlight how early-warning indicators of liquidity withdrawal can inform both market surveillance and execution. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.22985 |
| By: | Philippe van der Beck (Harvard Business School); Lorenzo Bretscher (Swiss Finance Institute - HEC Lausanne; Centre for Economic Policy Research (CEPR)); Julie Zhiyu Fu (Olin Business School, Washington University in St. Louis) |
| Abstract: | Asset prices are highly volatile, yet portfolio flows – changes in portfolio holdings – are relatively small. This reveals a fundamental tension between the price impact of portfolio flows and the agreement among investors: if price volatility is high while portfolio turnover is low, then either market participants largely agree with each other, or they are not sensitive to price changes (they are "inelastic"), resulting in large price impacts of portfolio flows. We formalize this trade-off and demonstrate that the ratio of return volatility to portfolio turnover provides a lower bound on price impact, conditional on the level of investor disagreement. Using several measures from survey data, we document substantial disagreement, implying meaningful lower bounds on price impacts. The bounds align closely with reduced-form estimates from a variety of quasi-experiments, such as price impacts from index reconstitutions. We demonstrate how these bounds vary across horizons, different assets, and at various levels of aggregation, including the aggregate stock market, and discuss their implications for asset pricing models. We argue that in such markets with high disagreement and price impact, observed trading activity is not peripheral but central to understanding asset price movements. |
| Keywords: | price impact, elasticity, portfolio turnover, investor disagreement |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2577 |