Artikel
Estimating and forecasting generalized fractional long memory stochastic volatility models
This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 10 ; Year: 2017 ; Issue: 4 ; Pages: 1-16 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
Methodological Issues: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Financial Econometrics
- Thema
-
stochastic volatility
GARCH models
Gegenbauer polynomial
long memory
spectral likelihood
estimation
forecasting
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Peiris, Shelton
Asai, Manabu
McAleer, Michael
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2017
- DOI
-
doi:10.3390/jrfm10040023
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Peiris, Shelton
- Asai, Manabu
- McAleer, Michael
- MDPI
Entstanden
- 2017
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