Chaotic Bayesian Inference: Strange Attractors as Risk Models for Black Swan Events

Crystal Rust

Papers from arXiv.org

Abstract: We introduce a new risk modeling framework where chaotic attractors shape the geometry of Bayesian inference. By combining heavy-tailed priors with Lorenz and Rossler dynamics, the models naturally generate volatility clustering, fat tails, and extreme events. We compare two complementary approaches: Model A, which emphasizes geometric stability, and Model B, which highlights rare bursts using Fibonacci diagnostics. Together, they provide a dual perspective for systemic risk analysis, linking Black Swan theory to practical tools for stress testing and volatility monitoring.

Date: 2025-09
New Economics Papers: this item is included in nep-ets and nep-mac
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