Arbeitspapier
Estimation of long memory in volatility using wavelets
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.
- Language
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Englisch
- Bibliographic citation
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Series: FinMaP-Working Paper ; No. 33
- Classification
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Wirtschaft
- Subject
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volatility
long memory
FIEGARCH
wavelets
Whittle
Monte Carlo
- Event
-
Geistige Schöpfung
- (who)
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Kraicova, Lucie
Barunik, Jozef
- Event
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Veröffentlichung
- (who)
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Kiel University, FinMaP - Financial Distortions and Macroeconomic Performance
- (where)
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Kiel
- (when)
-
2015
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Kraicova, Lucie
- Barunik, Jozef
- Kiel University, FinMaP - Financial Distortions and Macroeconomic Performance
Time of origin
- 2015
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