Copertina

GARCH-FIS: A Hybrid Forecasting Model with Dynamic Volatility-Driven Parameter Adaptation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Wen-Jing, Zhang, Da-Qing
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Autoencoder Enhanced Realised GARCH on Volatility Forecasting
di: Zhao, Qianli, et al.
Pubblicazione: (2024)

From GARCH to Neural Network for Volatility Forecast
di: Zhao, Pengfei, et al.
Pubblicazione: (2024)

The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models
di: Roszyk, Natalia, et al.
Pubblicazione: (2024)

GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets
di: Xu, Zeda, et al.
Pubblicazione: (2024)

highFIS
di: França, Daniel
Pubblicazione: (2026)

highFIS
di: França, Daniel
Pubblicazione: (2026)

highFIS
di: França, Daniel
Pubblicazione: (2026)

Orthogonal Subspace Clustering: Enhancing High-Dimensional Data Analysis through Adaptive Dimensionality Reduction and Efficient Clustering
di: Wen, Qing-Yuan, et al.
Pubblicazione: (2026)

Pairwise Markov Chains for Volatility Forecasting
di: Azeraf, Elie
Pubblicazione: (2024)

A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning
di: Sun, Shengjie, et al.
Pubblicazione: (2024)

Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting
di: Perekhodko, Anna, et al.
Pubblicazione: (2025)

PhaseNAS: Language-Model Driven Architecture Search with Dynamic Phase Adaptation
di: Kong, Fei, et al.
Pubblicazione: (2025)

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting
di: Pan, Licheng, et al.
Pubblicazione: (2025)

Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting
di: Medeiros, Heitor R., et al.
Pubblicazione: (2025)

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
di: Wang, Tingting, et al.
Pubblicazione: (2026)

PETAH: Parameter Efficient Task Adaptation for Hybrid Transformers in a resource-limited Context
di: Augustin, Maximilian, et al.
Pubblicazione: (2024)

Volatility Modeling via EWMA-Driven Time-Dependent Hurst Parameters
di: Athipatla, Jayanth
Pubblicazione: (2025)

Dynamic Low-Rank Sparse Adaptation for Large Language Models
di: Huang, Weizhong, et al.
Pubblicazione: (2025)

COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learning
di: Wang, Zian, et al.
Pubblicazione: (2024)

Adaptive regularization parameter selection for high-dimensional inverse problems: A Bayesian approach with Tucker low-rank constraints
di: Yang, Qing-Mei, et al.
Pubblicazione: (2026)

A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines
di: Chen, Yixiong
Pubblicazione: (2026)

Volatility Forecasting in Global Financial Markets Using TimeMixer
di: Li, Alex
Pubblicazione: (2024)

Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts
di: Dudek, Grzegorz, et al.
Pubblicazione: (2025)

Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation
di: Lin, Luxi, et al.
Pubblicazione: (2026)

Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
di: Dong, Zheng, et al.
Pubblicazione: (2024)

Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
di: Li, Zijian, et al.
Pubblicazione: (2024)

Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models
di: Yang, Xiao-Wen, et al.
Pubblicazione: (2026)

Dynamic Domain Adaptation-Driven Physics-Informed Graph Representation Learning for AC-OPF
di: Zhu, Hongjie, et al.
Pubblicazione: (2025)

A Hybrid Deep Learning based Carbon Price Forecasting Framework with Structural Breakpoints Detection and Signal Denoising
di: Ren, Runsheng, et al.
Pubblicazione: (2025)

KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation
di: Luo, Xindi, et al.
Pubblicazione: (2024)

UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting
di: Zhang, Da, et al.
Pubblicazione: (2025)

How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting
di: Su, Yiqi, et al.
Pubblicazione: (2026)

Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation
di: Angelini, Christopher, et al.
Pubblicazione: (2025)

Parameter Importance-Driven Continual Learning for Foundation Models
di: Wang, Lingxiang, et al.
Pubblicazione: (2025)

LiveTune: Dynamic Parameter Tuning for Feedback-Driven Optimization
di: Shabgahi, Soheil Zibakhsh, et al.
Pubblicazione: (2023)

XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility
di: Li, Xiaoming, et al.
Pubblicazione: (2023)

Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference
di: Min, Chao, et al.
Pubblicazione: (2024)

Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives
di: Dudek, Grzegorz, et al.
Pubblicazione: (2025)

TimeCatcher: A Variational Framework for Volatility-Aware Forecasting of Non-Stationary Time Series
di: Chen, Zhiyu, et al.
Pubblicazione: (2026)

Feed-Forward Latent Domain Adaptation
di: Bohdal, Ondrej, et al.
Pubblicazione: (2022)