Finding 4827Emerging EvidenceValidation V0
Researchers introduce GaMM, a novel hybrid model integrating GARCH representations with an MLP-based mixer, achieving superior high-frequency volatility forecasting, risk assessment, and reducing forecast errors and $VaR$ estimates across models.
82%Confidence
1Evidence objects
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Evidence trail
Supporting82% linkage confidence
Researchers introduce GaMM, a novel hybrid model integrating GARCH representations with an MLP-based mixer, achieving superior high-frequency volatility forecasting, risk assessment, and reducing forecast errors and $VaR$ estimates across models.
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Inspect source: High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model →This Finding was extracted from the configured corpus. It is versioned, traceable, and may evolve through editorial review or new corpus evidence.