Estimation of long memory in volatility using wavelets

Lucie Kraicova and Jozef Baruník

No 33, FinMaP-Working Papers from Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents

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.

Keywords: volatility; long memory; FIEGARCH; wavelets; Whittle; Monte Carlo (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ets
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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 (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:fmpwps:33

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