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B1501
Title: Robust recursive estimation of GARCH models Authors:  Radek Hendrych - Charles University (Czech Republic) [presenting]
Tomas Cipra - Charles University, Prague (Czech Republic)
Abstract: The generalized autoregressive conditional heteroscedasticity (GARCH) process is a particular modelling scheme, which is capable of forecasting the current level of volatility of financial time series. This classic benchmark model is designed to track changes in volatility of financial returns by using past squared measurements. Recently, recursive estimation methods suitable for this class of stochastic processes have been introduced in the literature. They undoubtedly represent attractive alternatives to the standard non-recursive estimation procedures with many practical applications. It might be truly advantageous to adopt numerically effective estimation techniques that can estimate and control such models in real time. However, abnormal observations (outliers) may occur in data. They may be caused by many reasons, e.g. by additive errors, measurement failures or management actions. Exceptional data points will influence the model estimation considerably if no specific action is taken. The aim is to propose and thoroughly examine a robust recursive estimation algorithm suitable for GARCH models. Monte Carlo experiments are performed in order to investigate the introduced approach. Moreover, real data examples are also discussed.