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.