Estimation of long memory in volatility using wavelets

Kraicová Lucie and Jozef Baruník
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Kraicová Lucie: Institute of Economic Studies, Charles University, Opletalova 26, 110 00 Prague, Czech Republic

Studies in Nonlinear Dynamics & Econometrics, 2017, vol. 21, issue 3, 22

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. In particular, a localized version of our estimator becomes attractive in small samples.

Keywords: FIEGARCH; long memory; Monte Carlo; volatility; wavelets; Whittle (search for similar items in EconPapers)
Date: 2017
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Related works:
Working Paper: Estimation of long memory in volatility using wavelets (2015)
Working Paper: Estimation of Long Memory in Volatility Using Wavelets (2014)
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DOI: 10.1515/snde-2016-0101

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