Early online detection of high volatility clusters using Particle Filters

Abstract

This work presents a novel online early detector of high-volatility clusters based on uGARCH models (a variation of the GARCH model), risk-sensitive particle-filtering-based estimators, and hypothesis testing procedures. The proposed detector utilizes Risk-Sensitive Particle Filters (RSPF) to generate an estimate of the volatility probability density function (PDF) that offers better resolution in the areas of the state-space that are associated with the incipient appearance of high-volatility clusters. This is achieved using the Generalized Pareto Distribution for the generation of particles. Risk-sensitive estimates are used by a detector that evaluates changes between prior and posterior probability densities via asymmetric hypothesis tests, allowing early detection of sudden volatility increments (typically associated with early stages of high-volatility clusters). Performance of the proposed approach is compared to other implementations based on the classic Particle Filter, in terms of its capability to track regions of the state-space associated to a greater financial risk. The proposed volatility cluster detection scheme is tested and validated using both simulated and actual IBM’s daily stock market data.