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
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

Dieses Objekt wird bereitgestellt von:
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

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