Selected work

Representative Papers

On Bonart's interpretation of the Square-Root Impact Law

Oct 07, 2026

This study investigates Bonart’s interpretation of the square-root impact law (SRIL), examining the consistency of its underlying assumptions with market microstructure mechanisms. Drawing on stochastic process theory and the Lillo-Mike-Farmer model, we reconstruct and simplify the original argumentation, developing a multi-agent propagator simulation to validate the market whitening hypothesis. Our results demonstrate that isolated meta-orders fail to satisfy the SRIL, and that markets require exceptionally high signal-processing efficiency to conform to this law. Furthermore, we propose falsifiable predictions based on flow hypotheses, revealing that diffusion paths necessitate a defined temporal origin to explain mean impact decay. By challenging the feasibility of anonymous trade attribution, this work offers novel perspectives for understanding market impact dynamics.

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Gaussian Flow-Matching Schedules: Implications for Sampling and Training

Sep 22, 2026

研究通过方向依赖性调度分解为方差路径和因子化，以优化采样动态及减少回归目标的方差。

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Non-Equilibrium Economics: A Physicist's Point of View

Jul 10, 2026

This study challenges the equilibrium paradigm of mainstream economics by investigating the endogenous origins of bubbles, crises, and inequality in economic systems. Drawing on complex systems theory, the authors develop a non-equilibrium dynamical “toy” model that integrates nonlinear dynamics and self-organized criticality to uncover three key mechanisms: path-dependent multiple equilibria, chaotic oscillations arising from dynamically inaccessible equilibria, and fragile stability induced by self-organized criticality. Remarkably, the model reproduces excessive volatility, endogenous crises, inflationary spirals, and persistent inequality—all without exogenous shocks—thereby offering a novel non-equilibrium explanation for macroeconomic instability and prompting a critical re-evaluation of conventional equilibrium-based frameworks.

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Covariance Shrinkage via Stochastic Interpolation

Jun 05, 2026

This work addresses the high statistical risk inherent in high-dimensional covariance estimation by reframing covariance shrinkage as a parameterized empirical risk minimization problem based on stochastic interpolation between source and target distributions. The proposed approach extends the theoretical foundations of classical shrinkage estimation through a synergistic integration of optimal transport couplings, eigenvector regularization induced by nonlinear flow maps, an early-stopping mechanism grounded in vector field regression, and an adaptive scheduling strategy. By unifying stochastic interpolation, neural estimators, and quadratic risk upper-bound analysis, the method demonstrates strong empirical performance on synthetic data and achieves superior regularization efficacy and estimation accuracy on real neuroimaging datasets.

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Recent publications

Latest Papers

On Bonart's interpretation of the Square-Root Impact Law

Oct 07, 2026

This study investigates Bonart’s interpretation of the square-root impact law (SRIL), examining the consistency of its underlying assumptions with market microstructure mechanisms. Drawing on stochastic process theory and the Lillo-Mike-Farmer model, we reconstruct and simplify the original argumentation, developing a multi-agent propagator simulation to validate the market whitening hypothesis. Our results demonstrate that isolated meta-orders fail to satisfy the SRIL, and that markets require exceptionally high signal-processing efficiency to conform to this law. Furthermore, we propose falsifiable predictions based on flow hypotheses, revealing that diffusion paths necessitate a defined temporal origin to explain mean impact decay. By challenging the feasibility of anonymous trade attribution, this work offers novel perspectives for understanding market impact dynamics.

0 citationsRead paper

Gaussian Flow-Matching Schedules: Implications for Sampling and Training

Sep 22, 2026

研究通过方向依赖性调度分解为方差路径和因子化，以优化采样动态及减少回归目标的方差。

0 citationsRead paper

Non-Equilibrium Economics: A Physicist's Point of View

Jul 10, 2026

This study challenges the equilibrium paradigm of mainstream economics by investigating the endogenous origins of bubbles, crises, and inequality in economic systems. Drawing on complex systems theory, the authors develop a non-equilibrium dynamical “toy” model that integrates nonlinear dynamics and self-organized criticality to uncover three key mechanisms: path-dependent multiple equilibria, chaotic oscillations arising from dynamically inaccessible equilibria, and fragile stability induced by self-organized criticality. Remarkably, the model reproduces excessive volatility, endogenous crises, inflationary spirals, and persistent inequality—all without exogenous shocks—thereby offering a novel non-equilibrium explanation for macroeconomic instability and prompting a critical re-evaluation of conventional equilibrium-based frameworks.

0 citationsRead paper

Covariance Shrinkage via Stochastic Interpolation

Jun 05, 2026

This work addresses the high statistical risk inherent in high-dimensional covariance estimation by reframing covariance shrinkage as a parameterized empirical risk minimization problem based on stochastic interpolation between source and target distributions. The proposed approach extends the theoretical foundations of classical shrinkage estimation through a synergistic integration of optimal transport couplings, eigenvector regularization induced by nonlinear flow maps, an early-stopping mechanism grounded in vector field regression, and an adaptive scheduling strategy. By unifying stochastic interpolation, neural estimators, and quadratic risk upper-bound analysis, the method demonstrates strong empirical performance on synthetic data and achieves superior regularization efficacy and estimation accuracy on real neuroimaging datasets.

0 citationsRead paper