Searcharxiv⌕ Search

arXiv · 2609.36631

A Spread-Gated Hawkes-Flocking Model for Best Bid and Ask Dynamics, with an Application to Limit Order Placement

Abstract

We study the joint dynamics of the best bid and ask prices with a spread-gated Hawkes-flocking model. The model tracks four types of best-quote movements: spread-narrowing movements are switched off when the spread is at its one-tick minimum, and a cross-side excitation term, whose activation depends on the prevailing spread, links the two sides of the book. We show that the process is non-explosive on every finite horizon, give an $O(N)$ recursive likelihood, and validate the maximum likelihood estimator by simulation. On real intraday limit order book data for two large-tick stocks, INTC and MSFT, the restriction that removes the cross-side term is rejected, and the full model improves fit substantially by AIC and BIC; the likelihood is multimodal on a single day, so estimation uses a multi-start search. As an application, we derive the closed-form optimal size of a single-period limit order placed at the best or second-best quote, given the model's next-event probabilities and externally supplied execution probabilities.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hyoeun Lee, Kiseop Lee. 2026-09-29. A Spread-Gated Hawkes-Flocking Model for Best Bid and Ask Dynamics, with an Application to Limit Order Placement. https://arxiv.org/abs/2609.36631

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Multi-Period Martingale Optimal Transport: Classical Theory, Neural Acceleration, and Financial Applications

This paper develops a computational framework for Multi-Period Martingale Optimal Transport (MMOT), addressing convergence rates, algorithmic efficiency, and financial calibration. Our contributions include: (1) Theoretical analysis: We establish discrete convergence rates of $O(\sqrt{Δt} \log(1/Δt))$ via Donsker's principle and linear algorithmic convergence of $(1-κ)^{2/3}$; (2) Algorithmic improvements: We introduce incremental updates ($O(M^2)$ complexity) and adaptive sparse grids; (3) Numerical implementation: A hybrid neural-projection solver is proposed, combining transformer-based warm-starting with Newton-Raphson projection. Once trained, the pure neural solver achieves a $1{,}597\times$ online inference speedup ($4.7$s $\to 2.9$ms) suitable for real-time applications, while the hybrid solver ensures martingale constraints to $10^{-6}$ precision. Validated on 12,000 synthetic instances (GBM, Merton, Heston) and 120 real market scenarios.

q-fin.CP↗

Synthetic American Option Pricing via Jump-HMM-Driven Heston Implied Volatility

Valuing American options along simulated stock paths requires an implied volatility (IV) for every option on every date. A stock-return model alone does not provide it. We built a simulator that assigned IV to American options on simulated dates for any chosen stock model. We fitted parametric and neural IV surfaces to vendor option quotes for 31 tickers. Each surface predicted IV from moneyness and time to expiration. Stock paths came from a jump hidden Markov model with capped daily returns. Each day, every option's implied variance moved partway toward its surface prediction, following the form of the Heston variance equation. Random shocks tended to raise IV when the stock fell. A binomial tree converted IV into option values and price sensitivities. Neural surfaces fitted by sector or ticker matched the quotes more closely than one parametric surface. Their errors still varied by date and carried into dollar prices. Repricing identical stock paths under five ways of updating IV changed option values before expiration, and the worst simulated short-position losses at a fixed horizon. Payoffs at expiration did not change. In forecasts of later Goldman Sachs and Eli Lilly option prices, stochastic IV did little better than fixed IV. Rerunning the forecasts with the realized stock paths pointed to stock prediction as a major source of option-price error. The hidden Markov model and an adaptive-volatility model predicted stock prices about as accurately as assuming no change, and tuning found no gain from predicting direction. The adaptive-volatility model improved predicted price ranges for Eli Lilly but not Goldman Sachs. The simulator supported reproducible comparisons of IV and stock assumptions but did not forecast better than simpler alternatives. Complete option histories and prices consistent across strikes are needed before simulated prices can replace market data.

q-fin.CP↗

Dynamic Inverse Optimization under Drift and Shocks: Theory, Regret Bounds, and Applications

Operational decisions often reflect priorities that evolve gradually and may shift abruptly after disruptions. We develop a dynamic inverse optimization framework for recovering time-varying preference parameters from observed allocation trajectories. The framework separates an offline temporally regularized trajectory formulation from an online mirror-descent estimator for streaming recovery, thereby linking inverse optimization with online convex optimization under nonstationarity. We establish sufficient conditions for local identifiability and existence, static regret of $O(\sqrt{T})$, dynamic regret of $O(\sqrt{T}(1+V_T))$ for the online estimator, and a residual-to-parameter stability bound governed by the conditioning of the projected inverse map. The analysis distinguishes uniqueness of the inverse explanation, conditioning of the recovery map, existence of a batch trajectory solution, and sequential performance under a moving comparator. Controlled experiments on two stylized allocation benchmarks examine noiseless recoverability, finite-horizon regret, gradual drift, observational noise, abrupt shocks, and temporal coupling. These experiments are interpreted as finite-sample diagnostics rather than proofs of asymptotic bounds. Across the tested settings, temporal coupling moderates the trade-off between static under-adaptation and excessive variation from independent period-by-period inversion. The resulting framework provides an interpretable foundation for online inverse optimization and nonstationary decision analytics under evolving priorities. It also clarifies when structural recovery and sequential adaptation require distinct guarantees.

q-fin.CP↗