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on Market Microstructure |
| By: | Tianzuo Hu |
| Abstract: | We propose a Neural Hidden Markov Model (HMM) with Adaptive Granularity Attention (AGA) for high-frequency order flow modeling. The model addresses the challenge of capturing multi-scale temporal dynamics in financial markets, where fine-grained microstructure signals and coarse-grained liquidity trends coexist. The proposed framework integrates parallel multi-resolution encoders, including a dilated convolutional network for tick-level patterns and a wavelet-LSTM module for low-frequency dynamics. A gating mechanism conditioned on local volatility and transaction intensity adaptively fuses multi-scale representations, while a multi-head attention layer further enhances temporal dependency modeling. Within this architecture, a Neural HMM with conditional normalizing flow emissions is employed to jointly model latent market regimes and complex observation distributions. Empirical results on high-frequency limit order book data demonstrate that the proposed model outperforms fixed-resolution baselines in predicting short-term price movements and liquidity shocks. The adaptive granularity mechanism enables the model to dynamically adjust its focus across time scales, providing improved performance particularly during volatile market conditions. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.20456 |
| By: | Akitada Kasahara (Graduate School of Economics, the University of Osaka); Masahiro Yamada (School of Management, Tokyo University of Science) |
| Abstract: | We estimate the causal effect of single-stock trading pauses on market quality using tick-by-tick order book data from the Tokyo Stock Exchange (TSE). Our instrumental variable exploits the TSE’s fixed yen-denominated triggering thresholds, which generate plausibly exogenous variation in the percentage distance to a trading pause across stocks with different price levels. Trading pauses significantly reduce post-event volatility, narrow quoted bid–ask spreads, and facilitate price discovery. Orderlevel analysis reveals the mechanism: during pauses, liquidity providers submit opposite-direction limit orders at aggressively priced levels that push the matching price toward reversal, generating the observed improvements. These effects are strongest for less frequently traded stocks, where incremental liquidity has the largest impact. However, the benefits are attenuated for highly volatile stocks with recent negative returns, particularly during broad market downturns. |
| Keywords: | Trading pauses, Circuit breakers, Volatility, Liquidity, Price discovery |
| JEL: | G12 G14 G18 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:osk:wpaper:2603 |
| By: | Lindner, Vincent; Lucke, Konrad; Pelizzon, Loriana |
| Abstract: | This policy paper examines the effects of the introduction of the MiFID II / MiFIR framework on the transparency regime for sovereign bonds. The main purpose of the framework is to provide markets with real-time information and lower market asymmetries, thereby increasing liquidity in the market. Combining regulatory transaction data from BaFin with MTS limit order book data, we find contrasting results. While mandatory trade reporting had the desired effect in over-the-counter markets, as it narrowed spreads and improved liquidity, the MTS limit order book tells a different story. Once quotes and trades became generally public, dealers reacted by widening spreads and curtailing their quoting agressiveness which ultimately undermined liquidity and deteriorated price discovery. These findings have important consequences for further market integration efforts under the Savings and Investment Union umbrella. Policymakers must carefully consider the unintended consequences of desirable policy outcomes, such as market transparency, and should tailor policy solutions to venue type. |
| Keywords: | MiFID II, MiFIR, transparency, liquidity, sovereign bonds |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewh:339599 |
| By: | Kyungsub Lee |
| Abstract: | This paper presents a method for forecasting limit order book durations using a self-exciting flexible residual point process. High-frequency events in modern exchanges exhibit heavy-tailed interarrival times, posing a significant challenge for accurate prediction. The proposed approach incorporates the empirical distributional features of interarrival times while preserving the self-exciting and decay structure. This work also examines the stochastic stability of the process, which can be interpreted as a general state-space Markov chain. Under suitable conditions, the process is irreducible, aperiodic, positive Harris recurrent, and has a stationary distribution. An empirical study demonstrates that the model achieves strong predictive performance compared with several alternative approaches when forecasting durations in ultra-high-frequency trading data. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.00346 |
| By: | Patrick Noble; Mathieu Rosenbaum; Saad Souilmi |
| Abstract: | We introduce a practical, interactive simulator of the limit order book for large-tick assets, designed to produce realistic execution, costs, and P&L. The book state is projected onto a tractable representation based on spread and volume imbalance, enabling robust estimation from market data. Event timing is calibrated to reproduce the fine-scale temporal structure of real markets, revealing a pronounced mode at exchange round-trip latency consistent with simultaneous reactions and latency races among participants. We further incorporate a feedback mechanism that accumulates signed trade flow through a power-law decay kernel, reproducing both concave market impact during execution and partial post-trade reversion. Across several stocks and strategy case studies, the simulator yields realistic behavior where profitability becomes highly sensitive to execution parameters. We present the approach as a practical recipe: project, estimate, validate, adapt, for building realistic limit order book simulations. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.24137 |
| By: | Lars Winkelmann; Wenying Yao |
| Abstract: | This paper examines how regulatory interventions in high-frequency financial markets affect price discovery. We focus on Breaking news, where dynamic circuit breakers trigger trading halts immediately after the release of macroeconomic fundamentals. Within a high-frequency signal-in-noise model, we show that triggering rules complicate statistical inference for the price impact of news, rendering conventional non-parametric jump estimators inconsistent. Building on this insight, we develop a regression-based test for fundamental pricing that accounts for non-vanishing transition times. The test compares transition price changes to efficient jumps implied by observable factors. Our empirical analysis of CME E-mini S\&P 500 futures shows that Breaking news are associated with systematic deviations from fundamental pricing, predominantly in the form of overshooting. Our findings highlight a regulatory trade-off: the appeal of simple and transparent circuit breaker rules must be weighed against their cost of preventing fundamentals from being priced contemporaneously, thereby creating adverse incentives and introducing distortions. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.22835 |