a-hybrid-garch-bilstm-kan-model-for-crude-oil-price-forecasting

A Hybrid GARCH-BiLSTM-KAN Model for Crude Oil Price Forecasting: Capturing Volatility, Temporal Dependencies, and Nonlinear Dynamics

Crude oil prices, as a cornerstone of global energy markets, exhibit intricate dynamics---including volatility clustering, asymmetric temporal ...

comparative-evaluation-deep-learning-model-complexity-for-forecasting

Comparative Evaluation of Deep Learning Model Complexity for Forecasting Non-Ferrous Metal Prices

This study examines whether increasing architectural complexity improves forecasting accuracy in deep learning&#8211;based financial models. Using daily ...

Guangzhou College of Technology and Business

This study examines volatility spillovers between Chinese and U.S. equity markets by developing a comprehensive framework that captures asymmetric volatility, extreme co-movements, and dynamic correlations. We propose an integrated methodology combining EGARCH models with Student-t innovations, a Student-t copula, and a Dynamic Conditional Correlation (DCC) structure. Using daily returns of the Hang Seng Index (HSI) and the S&P 500, our analysis reveals three principal findings. First, the EGARCH model effectively captures the pronounced leverage effect and fat-tailed distributions characteristic of both markets. Second, the Student-t copula demonstrates the best fit among competing specifications, indicating significant symmetric tail dependence between the two markets. Third, time-varying correlations exhibit high persistence, rising during crises yet remaining within a moderate range. Crucially, out-of-sample forecasting shows that our unified framework achieves superior predictive accuracy relative to standard benchmarks. These findings provide valuable insights for investors designing hedging strategies, exchanges determining margin requirements, and policymakers monitoring financial contagion. Our approach offers a robust tool for analyzing volatility transmission between developed and emerging markets.

ArticleTotal : 2	Year
Publication title Cited by 1	2025
Publication title	2026

Jin Zeng

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