arXiv Machine Learning

Susceptible Reservoir Architectures for Regime-Conditional Volatility Forecasting

arXiv:2607. 22491v1 Announce Type: new Abstract: Volatility forecasting is dominated by persistence and measurement noise, leaving limited residual structure for nonlinear models to exploit.

arXiv Machine Learning

Aug 4

Latent-Regime Bias Auditing for Volatility Forecasting

arXiv:2608. 01599v1 Announce Type: new Abstract: Volatility forecasts are commonly evaluated with aggregate accuracy metrics such as RMSE and MAE, but these metrics can hide conditional failures that matter for risk management.

By Arthur Chagas, Pedro Bento, Yan Aquino, Arthur Buzelin, Wagner Meira Jr., Cristiano Arbex Valle

safety

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.

By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang

llms multimodal benchmarks safety

arXiv Machine Learning

6d ago

Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting

arXiv:2608. 12251v1 Announce Type: cross Abstract: Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training.

By Junyi Ye, Gargi Vijay Borde

arXiv AI

Jul 7

A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference

arXiv:2603. 09789v3 Announce Type: replace-cross Abstract: Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data.

By Yixiong Chen

efficiency

arXiv Machine Learning

Jul 28

Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark

arXiv:2607. 24065v1 Announce Type: cross Abstract: In this study, we developed and evaluated four conditional energy-based forecasting architectures: a classical Gaussian-Bernoulli CRBM, a hybrid quantum-classical QCRBM, a full-register QQRBM, and a lag-feature QFeatureQRBM with complete derivations of their conditional distributions, Contrastive-Divergence gradients, and hybrid training, bridging the energy-based formulation and the implementation-level quantum computation.

By Gerhard Hellstern, Danyal Maheshwari, Martin Zaefferer, Martin Braun, Tanja D\"ohler

benchmarks

arXiv Machine Learning

Aug 4

Conditional Deep Levy Models for Exotic Derivatives: History-Aware Path Generation and P-Q Payoff Diagnostics

arXiv:2509. 13374v2 Announce Type: replace-cross Abstract: We develop and audit a history-aware financial path generator based on Denoising Levy Probabilistic Models (DLPMs) for conditional equity-index path generation.

By Helin Zhao, Junchi Shen

diffusion benchmarks

arXiv AI

Jun 29

Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting

arXiv:2606. 27688v1 Announce Type: cross Abstract: In financial forecasting, predictive performance depends not only on which model is trained, but also on how the trained model is deployed.

By Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho

arXiv Machine Learning

Jul 21

Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware

arXiv:2510. 13634v2 Announce Type: replace Abstract: Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints.

By Wissal Hamhoum, Soumaya Cherkaoui, Jean-Frederic Laprade, Ola Ahmad, Shengrui Wang

arXiv AI

Jul 21

A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations

arXiv:2607. 16358v1 Announce Type: cross Abstract: This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F).

By Sanjay Chakraborty, Fredrik Heintz

efficiency benchmarks

arXiv AI

Jun 9

Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman

arXiv:2606. 09104v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data.

By Daniil Mikriukov (University of Liverpool, Xi'an Jiaotong-Liverpool University), Ruoyu Sun (Xi'an Jiaotong-Liverpool University), Angelos Stefanidis (Xi'an Jiaotong-Liverpool University), Jionglong Su (Xi'an Jiaotong-Liverpool University), Zhengyong Jiang (Xi'an Jiaotong-Liverpool University)

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jul 13

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility

arXiv:2510. 04487v5 Announce Type: replace Abstract: While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs).

By Willa Potosnak, Malcolm Wolff, Mengfei Cao, Ruijun Ma, Tatiana Konstantinova, Dmitry Efimov, Michael W. Mahoney, Boris Oreshkin, Kin G. Olivares

llms benchmarks

arXiv Machine Learning

Jun 2

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting

arXiv:2606. 02117v1 Announce Type: cross Abstract: Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in future observations.

By Tingting Wang, Yunyi Zhang, Benyou Wang

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