Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/108901
Authors: 
Kraicova, Lucie
Barunik, Jozef
Year of Publication: 
2015
Series/Report no.: 
FinMaP-Working Paper 33
Abstract: 
This work studies wavelet-based Whittle estimator of the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroscedasticity (FIEGARCH) model, often used for modeling long memory in volatility of financial assets. The newly proposed estimator approximates the spectral density using wavelet transform, which makes it more robust to certain types of irregularities in data. Based on an extensive Monte Carlo study, both behaviour of the proposed estimator and its relative performance with respect to traditional estimators are assessed. In addition, we study properties of the estimators in presence of jumps, which brings interesting discussion. We find that wavelet-based estimator may become an attractive robust and fast alternative to the traditional methods of estimation.
Subjects: 
volatility
long memory
FIEGARCH
wavelets
Whittle
Monte Carlo
Document Type: 
Working Paper
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