Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230822
Authors: 
Feng, Yuanhua
Härdle, Wolfgang Karl
Year of Publication: 
2020
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2020-016
Abstract: 
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.
Subjects: 
P-spline smoother
smoothing parameter selection
P-Spline-GARCH
strong mixing
value at risk
expected shortfall
JEL: 
C14
C51
Document Type: 
Working Paper

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