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This is a Quasi-Experimental study.

September 17, 2025EntropyOpen Access

A Quasi-Monte Carlo Method Based on Neural Autoregressive Flow

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The proposed method improves sampling accuracy in complex distributions, suggesting its utility in financial modeling and inference tasks.

This new transport quasi-Monte Carlo method, utilizing neural autoregressive flow, enhances sampling precision.
Numerical experiments show improved sampling accuracy over standard Monte Carlo-based normalizing flows in complex settings.
The method employs Kullback–Leibler divergence for optimization and incorporates a hidden-variable mechanism for computational efficiency.
Application to A-share stock data indicates reliable performance in predicting semiannual returns and capturing covariance structures.

Wei et al. (2025) studied this question.

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The proposed method improves sampling accuracy in complex distributions, suggesting its utility in financial modeling and inference tasks.