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Time series modelling with semiparametric factor dynamics

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High-dimensional regression problems which reveal dynamic behavior are typically analyzed by time propagation of a few number of factors. The inference on the whole system is then based on the low-dimensional time series analysis. Such highdimensional problems occur frequently in many different fiel...

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Bibliographische Detailangaben
Weitere Verfasser: Borak, Szymon (BerichterstatterIn) , Härdle, Wolfgang (BerichterstatterIn) , Mammen, Enno (BerichterstatterIn) , Park, Byeong U. (BerichterstatterIn)
Dokumenttyp: Book/Monograph Arbeitspapier
Sprache:Englisch
Veröffentlicht: Berlin SFB 649, Economic Risk 26 Apr. 2007
Schriftenreihe:SFB 649 discussion paper 2007,023
In: SFB 649 discussion paper (2007,023)

Schlagworte:
Arbeitspapier
Graue Literatur
Online-Zugang:Verlag, Volltext: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2007-023.pdf
Download aus dem Internet, Stand 19.02.2008, Volltext: http://hdl.handle.net/10419/25195
Volltext
Verfasserangaben:Szymon Borak; Wolfgang Härdle; Enno Mammen; Byeong U. Park

MARC

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520 |a High-dimensional regression problems which reveal dynamic behavior are typically analyzed by time propagation of a few number of factors. The inference on the whole system is then based on the low-dimensional time series analysis. Such highdimensional problems occur frequently in many different fields of science. In this paper we address the problem of inference when the factors and factor loadings are estimated by semiparametric methods. This more flexible modelling approach poses an important question: Is it justified, from inferential point of view, to base statistical inference on the estimated times series factors? We show that the difference of the inference based on the estimated time series and 'true' unobserved time series is asymptotically negligible. Our results justify fitting vector autoregressive processes to the estimated factors, which allows one to study the dynamics of the whole high-dimensional system with a low-dimensional representation. We illustrate the theory with a simulation study. Also, we apply the method to a study of the dynamic behavior of implied volatilities and discuss other possible applications in finance and economics. -- semiparametric models ; factor models ; implied volatility surface ; vector autoregressive process ; asymptotic inference 
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