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Hugging Face Trending Papers

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes

Accurate option prices do not imply accurate recovery of the latent risk-neutral density. We study this distinction with two complementary benchmarks.

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arXiv Machine Learning
Oct 23

Doubly Robust Distributionally Robust Offline Contextual Pricing

The paper introduces a doubly robust framework for distributionally robust offline contextual pricing, addressing challenges of distributional shifts and continuous actions. It proposes a localized DR estimator that is computationally efficient and semiparametrically optimal, and a learning algorithm that exploits demand noise smoothness to achieve finite‑sample regret bounds of τ^{-σ/(2σ+1)} for smoothness orders s=1,2. Experiments across varying distribution shifts demonstrate the effectiveness of the proposed methods.

By Min Xu, Xinyi Yin, Yunfan Zhang, Yuxuan Han, Houcai Shen, Caihua Chen
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arXiv AI
Oct 19

OrderFlowDiT: Transport-Regularized Diffusion Transformer for Probabilistic Limit-Order Supply Forecasting

OrderFlowDiT is a conditional Diffusion Transformer designed to forecast limit‑order volume over the next five minutes, jointly modeling price placement, total volume, and predictive uncertainty. The model uses unnormalized bid and ask volume‑at‑price fields and incorporates an unbalanced optimal‑transport (UOT) auxiliary loss to better capture price displacement and volume information. In a validation study across 30 U.S. equities, adding UOT reduced transport cost by 45.8% and transport energy score by 26.1% compared to mean‑squared‑error training, improving accuracy for every stock.

By Jung Hun Phee, Tucker Balch
llmsdiffusion
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arXiv Machine Learning
Oct 9

Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark

The paper introduces the first multi‑horizon benchmark for forecasting resale prices of second‑hand electronics, comparing eleven statistical and deep learning models on a large Polish marketplace dataset covering 100+ smartphone and laptop models from January 2022 to March 2025. Across horizons from 1 to 365 days, N‑BEATS consistently outperforms all statistical baselines, achieving an 8.51% MAPE at 365 days versus 14.94% for the best baseline—a 43% reduction—while all models converge to about 0.72% MAPE for short horizons. A single N‑BEATS model trained for the 365‑day horizon generalizes to all shorter horizons, eliminating the need for horizon‑specific models, and N‑BEATS and N‑HiTS show superior hyperparameter stability.

By Mateusz Buczy\'nski, Micha{\l} Wo\'zniak, Konrad Kaczy\'nski, Anna Wr\'oblewska, Sebastian Kuk
benchmarks
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arXiv Machine Learning
Sep 11

Halo: Improving forecast accuracy through heteroscedastic estimation

Halo is a modification to existing deep forecasters that adds a second output for estimating the scale of the predicted distribution, trained with a matching negative log likelihood. Experiments on five electricity price markets show that Halo improves mean squared error and mean absolute error in 28 of 30 model‑market‑metric comparisons, with average MSE reductions of 2.6% to 16.5% and MAE reductions of 1.7% to 11.0%. The study finds that the source of the scale estimate is less important than the fact that the network estimates scale, and that the improvement persists without retuning hyperparameters.

By Adam Cataldo
llmsbenchmarks
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arXiv Machine Learning
Sep 10

Asymptotically-informed neural networks for Black-Scholes implied volatility computation

The paper introduces asymptotically-informed neural‑network architectures for computing Black‑Scholes implied volatility. By learning a trainable partition of the price‑log‑moneyness domain and combining specialised local approximations, the models outperform standard feed‑forward networks across a wide range of parameters. The neural‑network outputs also serve as highly accurate initial guesses for a third‑order Householder scheme, enabling near machine‑precision results after only two refinement iterations.

By Samira Amiriyan, Youness Boutaib
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arXiv Machine Learning
Oct 15

Beyond Time Induction: Structured Conditional Neural Solvers for Parametric Stochastic-Volatility Pricing

The paper introduces BeTINN, a structured conditional neural network architecture that extends time‑induced modulation with multi‑directional time‑parameter conditioning and a joint residual pathway. BeTINN is evaluated on the Heston stochastic‑volatility PDE, showing improved accuracy for both option values and key sensitivity metrics compared to baseline models. Ablation studies highlight the contributions of each architectural component, and the authors observe that physics‑based validation loss does not fully capture derivative fidelity, underscoring the need for derivative‑aware evaluation when sensitivities are important.

By Jose Miguel Lara Rangel, Mixtli Vazquez Mendoza
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