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Bayesian Forecasting of Dynamic Extreme Quantiles
In this paper, we provide a novel Bayesian solution to forecasting extreme quantile thresholds that are dynamic in nature. This is an important problem in many fields of study including climatology, structural engineering, and finance. We utilize results from extreme value theory to provide the backdrop for developing a state-space model for the unknown parameters of the observed time-series. To solve for the requisite probability densities, we derive a Rao-Blackwellized particle filter and,doi:10.3390/forecast3040045 fatcat:tahsdadgjvgofmxjkmf66ltare