market microstructure analysis

This paper investigates the impact of market microstructure noise on the dynamic pricing of assets and European options. Addressing the limitation of existing models—which neglect the dual perturbation of noise on both drift and volatility—we propose two novel frameworks: (i) a continuous-time Black–Scholes–Merton model augmented with noise-corrected dynamic risk-neutral measures, and (ii) an extension of the Grossman–Stiglitz static framework into the first dynamic discrete binomial tree model capable of jointly capturing noise-induced distortions in drift and volatility. Methodologically, we integrate equivalent martingale measure transformations, high-frequency data-driven parameter estimation, and noise separation techniques. Empirically, our models uniquely identify noise parameters and significantly improve in-sample and out-of-sample fit of price dynamics under high-frequency observation. The results provide both theoretical foundations and implementable tools for option pricing and risk management in noisy market environments.

This paper addresses jump detection in high-frequency order prices from limit order books corrupted by one-sided (biased) market microstructure noise. Method: We propose the first global jump testing framework tailored to one-sided noise, constructing a test statistic grounded in extreme value theory and rigorously deriving its asymptotic distribution; jump locations are precisely identified via local order statistics, while jump sizes are robustly estimated using pointwise consistent volatility estimation. Contribution/Results: The test is proven consistent with asymptotically optimal convergence rate, breaking the detection lower bound of conventional additive-noise models and substantially enhancing sensitivity to small jumps. Simulation and empirical studies demonstrate superior sensitivity and reliability over state-of-the-art methods, significantly improving intraday order-flow micro-jump detection rates.

This paper addresses price impact modeling and the feasibility of round-trip arbitrage in financial markets. Methodologically, it pioneers the application of stochastic thermodynamics to finance: trading cycles are formalized as nonequilibrium thermodynamic processes, price impact is identified with dissipated work, and market noise is mapped onto thermal fluctuations. Building on this analogy, the authors formulate a “Financial Second Law,” proving that under convex price impact, the expected profit of any round-trip trading strategy is nonpositive. Leveraging tools from convex analysis, Gibbs measures, and statistical ensembles, they establish a bridge between macroscopic market constraints and microscopic trade structures, deriving testable no-arbitrage inequalities and closed-form solutions for canonical strategies. The results uncover the physical underpinnings of market efficiency and provide a unified theoretical foundation—and empirically verifiable pathway—for no-arbitrage conditions.

This study investigates how institutional liquidity provision affects bid-ask spreads, price discovery efficiency, and the welfare of slower traders in prediction markets. Addressing the challenge in existing literature of disentangling liquidity injection channels from causal identification, the authors develop a market quality analysis framework and innovatively employ a synthetic market microstructure experimental approach to conduct stress tests. The findings reveal that distinct liquidity mechanisms—such as market maker coverage, incentive schemes, and automation—operate through significantly different pathways. Moreover, aggregate improvements in liquidity do not uniformly benefit all participants; particularly under informational shocks or extreme market conditions, the welfare gains for disadvantaged traders are markedly limited. These results highlight the heterogeneous effects of liquidity policies and offer critical insights for market design.

This study challenges the conventional view that liquidity, supply, and demand are fundamental economic variables, arguing instead that they emerge from the geometric structure induced by order book observations. By modeling the market as an expanding relational system devoid of predefined metrics, time, or price coordinates, and applying spectral embedding of the graph Laplacian to obtain a one-dimensional projection, the authors derive a price-like coordinate and a corresponding liquidity distribution. Remarkably, this approach reproduces canonical order book regularities without invoking assumptions about agent behavior. Using high-frequency Level II data from U.S. equities, the research demonstrates the cross-asset universality of a cumulative gamma-shaped liquidity profile, with information criteria confirming its superior fit compared to existing models.

This study addresses how dynamic circuit breakers in high-frequency markets disrupt price discovery following macroeconomic news announcements, rendering conventional nonparametric jump estimators inconsistent. The authors develop a high-frequency signal-noise model to elucidate the regulatory trade-off between circuit breaker rules and efficient price discovery. They propose a novel regression-based test that identifies mispricing by comparing transient price movements during trading halts with the latent jumps implied by observable fundamentals. This approach accommodates non-vanishing transition durations and overcomes the inconsistency of traditional estimators. Empirical analysis using CME E-mini S&P 500 futures data reveals pervasive price overshooting after major news releases, indicating that current circuit breaker mechanisms impede the immediate incorporation of information into prices, thereby inducing pricing distortions and perverse incentives.

This study addresses the lack of a systematic understanding of market efficiency and return structures in automated market makers (AMMs) under the dynamic interaction between liquidity providers (LPs) and arbitrageurs. The authors develop a dynamic equilibrium framework for constant function market makers (CFMMs) that integrates slippage, trading fees, price impact, noise trading, endogenous gas fees, and time-varying volatility to capture strategic interactions among heterogeneous participants. They uncover an inherent bid–ask asymmetry in CFMMs, demonstrate that liquidity provision is strictly dominated in a pure arbitrage environment, and establish a non-degenerate interior equilibrium incorporating execution costs, which explains the inverted-U relationship between liquidity supply and volatility. Combining dynamic game-theoretic modeling, closed-form solutions, and on-chain data calibration, the paper empirically validates asymmetric price impact and non-monotonic optimal liquidity provision, offering theoretical foundations for AMM mechanism design and LP strategies.

This study investigates stable cross-asset microstructural patterns in cryptocurrency limit order books and their predictive power for short-term returns. Leveraging second-level data from Binance Futures, the authors employ a unified CatBoost framework with a direction-aware GMADL objective function and time-series cross-validation to systematically analyze the stability of feature importance and SHAP dependence across multiple assets. Findings are interpreted through the lens of classical market microstructure theory. Notably, the paper empirically validates adverse selection theory by contrasting market-making and taker strategies during flash crash events, thereby uncovering systemic risks inherent in algorithmic trading. The research reveals highly consistent and transferable feature patterns across assets of varying market capitalizations—such as BTC and LTC—and demonstrates their practical trading viability through conservative order book backtesting.

This study investigates how large language model–driven AI traders form expectations and influence asset market bubbles. By constructing a multi-agent simulation system within an open-outcry market environment, the research simulates trading behaviors and, for the first time, leverages interpretable textual reasoning to uncover the underlying decision-making mechanisms of AI agents. Through prompt engineering, the study implements causal interventions targeting specific behavioral biases. The framework successfully replicates established experimental market phenomena—such as the predictive power of excess demand on price dynamics and the positive correlation between belief dispersion and trading volume—and demonstrates effective control over bubble intensity. These findings offer a novel methodological approach and empirical evidence for understanding market dynamics in settings where AI agents actively participate.

This study addresses the challenge of modeling emergent behaviors in electronic financial markets arising from trader interactions, which are poorly captured by conventional automated reasoning approaches. To tackle this issue, the work proposes a systematic research program that integrates realistic market mechanisms with formal rigor, deliberately avoiding oversimplifying assumptions. By synergistically combining formal methods and computer-aided reasoning techniques, the authors develop an alternative framework tailored for modeling and analyzing complex financial systems. This framework establishes a computable formal foundation for understanding emergent phenomena in financial markets, elucidating their sources of complexity while opening scalable pathways for future research in market mechanism design and intelligent regulation.