Arbeitspapier
A data-driven P-spline smoother and the P-Spline-GARCH models
Penalized spline smoothing of time series and its asymptotic properties are studied. A data-driven algorithm for selecting the smoothing parameter is developed. The proposal is applied to de ne a semiparametric extension of the well-known Spline- GARCH, called a P-Spline-GARCH, based on the log-data transformation of the squared returns. It is shown that now the errors process is exponentially strong mixing with nite moments of all orders. Asymptotic normality of the P-spline smoother in this context is proved. Practical relevance of the proposal is illustrated by data examples and simulation. The proposal is further applied to value at risk and expected shortfall.
- Sprache
-
Englisch
- Erschienen in
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Series: IRTG 1792 Discussion Paper ; No. 2020-016
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Model Construction and Estimation
- Thema
-
P-spline smoother
smoothing parameter selection
P-Spline-GARCH
strong mixing
value at risk
expected shortfall
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Feng, Yuanhua
Härdle, Wolfgang Karl
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (wo)
-
Berlin
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:24 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Feng, Yuanhua
- Härdle, Wolfgang Karl
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Entstanden
- 2020