Estimation of Long Memory in Volatility Using Wavelets

Jozef Baruník and Lucie Kraicová ()
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Lucie Kraicová: Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nábreží 6, 111 01 Prague 1, Czech Republic

No 2014/33, Working Papers IES from Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies

Abstract: In this work we focus on the application of wavelet-based methods in volatility modeling. We introduce a new, wavelet-based estimator (wavelet Whittle estimator) of a FIEGARCH model, ARCH-family model capturing long-memory and asymmetry in volatility, and study its properties. Based on an extensive Monte Carlo experiment, both the behavior of the new estimator in various situations and its relative performance with respect to two more traditional estimators (maximum likelihood estimator and Fourier-based Whittle estimator) are assessed, along with practical aspects of its application. Possible solutions are proposed for most of the issues detected, including suggestion of a new speci cation of the estimator. This uses maximal overlap discrete wavelet transform, which improves the estimator perfor- mance, as we show in the experiment extension. Next, we study all the estimators in case of a FIEGARCH-Jump model, which brings interesting insights to their mechanism. We conclude that, after optimization of the estimation setup, the wavelet-based estimator may become an attractive robust alternative to the traditional methods

Keywords: volatility; long memory; FIEGARCH; wavelets; Whittle; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C13 C18 C51 G17 (search for similar items in EconPapers)
Pages: 81ages
Date: 2014-09, Revised 2014-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Journal Article: Estimation of long memory in volatility using wavelets (2017)
Working Paper: Estimation of long memory in volatility using wavelets (2015)
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