Arbeitspapier
Estimating persistence in the volatility of asset returns with signal plus noise models
This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by Robinson (1995a), and shown by Arteche (2004) to be consistent and asymptotically normal in the context of signal plus noise models. Daily data on the NASDAQ index are analysed. The results suggest that volatility has a component of long- memory behaviour, the order of integration ranging between 0.3 and 0.5, the series being therefore stationary and mean-reverting.
- Sprache
-
Englisch
- Erschienen in
-
Series: DIW Discussion Papers ; No. 1006
- Klassifikation
-
Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Fractional integration
long memory
stochastic volatility
asset returns
Kapitalertrag
Volatilität
Zeitreihenanalyse
Signalling
Noise Trading
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Caporale, Guglielmo Maria
Gil-Alana, Luis A.
- Ereignis
-
Veröffentlichung
- (wer)
-
Deutsches Institut für Wirtschaftsforschung (DIW)
- (wo)
-
Berlin
- (wann)
-
2010
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Caporale, Guglielmo Maria
- Gil-Alana, Luis A.
- Deutsches Institut für Wirtschaftsforschung (DIW)
Entstanden
- 2010
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