Information-Complexity Alignment for Stable Volatility Forecasting: A Model-Agnostic Framework with Regime Diagnostics

Abstract excerpt

<title>Abstract</title> <p>Forecasting financial volatility increasingly relies on complex models and high-dimensional information, yet greater complexity does not necessarily yield stable or interpretable behavior. Existing evaluation approaches emphasize predictive accuracy while offering limited insight into when and why complexity improves or destabilizes forecasting systems. This paper proposes the Informati...

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• Machine Learning
• Models, Economic
• Phenotype

Identifiers and source

Literature Corpus work

3d456ddd-3476-5890-a4e8-a851a6816d4e

DOI

10.21203/rs.3.rs-9060439/v1