Publication detail

Estimation of Long Memory in Volatility Using Wavelets

Author(s): doc. PhDr. Jozef Baruník Ph.D.,
Mgr. Lucie Kraicová ,
Type: IES Working Papers
Year: 2014
Number: 33
ISSN / ISBN:
Published in: IES Working Papers 33/2014
Publishing place: Prague
Keywords: volatility, long memory, FIEGARCH, wavelets, Whittle, Monte Carlo
JEL codes: C13, C18, C51, G17
Suggested Citation: Baruník J., Kraicová L. (2014). “Estimation of Long Memory in Volatility Using Wavelets” IES Working Paper 33/2014. IES FSV. Charles University.
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 behavior 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.
Downloadable: wp_2014_33_Barunik_Kraicova

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