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on Market Microstructure |
| By: | Li, Z. M.; Linton, O. B.; Zhai, Y.; Zhang, H. |
| Abstract: | We propose a new family of liquidity measures—including order imbalance metrics—based on the dispersion and persistence of transitory gaps between transaction prices and the underlying efficient price. We devise an estimation method that renders these latent gaps observable, allowing plug-in estimates of the new measures from intraday trades alone, along with an inference method that allows us to quantify the sampling uncertainty in our estimates. We apply the approach to the S&P 500 equity portfolio, as well as to individual stocks. We use event study methodology to capture heterogeneous liquidity responses to FOMC announcements, which reveals distinct order-persistence patterns on surprise versus non-surprise days, highlighting how markets anticipate and react to monetary policy via the liquidity channel. |
| Keywords: | Market liquidity, FOMC Announcements, Spot Estimation, Monetary Policy Surprises, Order Imbalance, High-Frequency Identification |
| Date: | 2026–01–18 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2639 |
| By: | Konrad Och\k{e}dzan; Nino Antulov-Fantulin |
| Abstract: | This paper examines the impact of market informedness on the profitability of market makers. In contrast to the existing literature, the analysis is conducted in a complex market environment featuring heterogeneous market-making agents that differ in terms of information sets and aversion to inventory risk, endogenous price formation, exogenous fundamental value dynamics, and self-exciting market order flow. The paper also establishes finite-horizon stability guarantees for the resulting state-dependent Hawkes market-taker process, including non-explosion, exponential mispricing integrability, occupation-time bounds, and a pathwise mispricing tail estimate. To address the market-making problem, the study employs a reinforcement learning framework based on the multi-agent proximal policy optimization (MAPPO) algorithm in a centralized training with decentralized execution (CTDE) setting. The study shows that informed market order flow is particularly dangerous in poorly informed markets, leading to severe adverse-selection risk. Although the complex market dynamics together with stochastic training give rise to locally non-monotonic outcomes, the results nevertheless reveal an overall upward trend in market makers' profitability as market informedness increases, suggesting that price discovery resulting from higher market informedness offsets the negative impact of adverse selection. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.05882 |
| By: | Robert Boyce; Eyal Neuman |
| Abstract: | We model a market with multiple dealers who compete for client order flow by dynamically updating their bid and ask quotes for a risky asset. Dealers aim to maximise expected profits while controlling inventory risk by skewing their quotes to attract offsetting order flow (internalisation) or by directly offloading positions in the market (externalisation). Using a variational approach, we derive a closed-form equilibrium for the resulting Nash competition, shedding light on key features of dealer market dynamics. We show that dealers relying on internalisation are compelled to increase their externalisation activity when competing with externalising dealers. This strategic shift in equilibrium leads to significantly higher hedging costs for all dealers and substantially wider spreads for clients. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.06413 |