Analysis of cognitive constraints of traditional financial risk models

Abstract

This article systematically analyze show cognitive constraints transform individual parameter estimation errors into systemic tail risk througha three-step process: theory, empirical evidence, and institutional frameworks. It then proposes actionable regulatory and industry tools. First, at the theoretical level, this article integrates bounded rationality, salience theory, and information theory to construct a "cognitive load-spectral entropy gap-tail exception" analytical framework. Second, at the empirical level, it integrates cross-lingual text, neuroimaging, and high-frequency trading data to validate the predictive power of cognitive proxies for VaR/ES exceptions. Finally, at the institutional level, it reviews the latest proposals from the Bureau of Internal Revenue (BIS) and the Federal Reserve, and designsa "cognitive augmentation verification layer" (CAVL) and an industry cockpit.