A functional version of the arch model created by Siegfried Hörmann, Lajos Horváth and Ron Reeder

By:

Hörmann, Siegfried [author]

Contributor(s):

Material type: Text

TextSeries: Cambridge: Cambridge University Press, 2013 ; Volume 29, number 2Cambridge: Cambridge University Press, 2013Content type:

text

Media type:

unmediated

Carrier type:

volume

ISSN:

02664666

Subject(s):

Time series analysis -- Estimation theory -- | Commodity exchange

LOC classification:

HB139.T52 ECO

Online resources:

Click here to access online

Abstract: Improvements in data acquisition and processing techniques have led to an almost continuous flow of information for financial data. High-resolution tick data are available and can be quite conveniently described by a continuous-time process. It is therefore natural to ask for possible extensions of financial time series models to a functional setup. In this paper we propose a functional version of the popular autoregressive conditional heteroskedasticity model. We will establish conditions for the existence of a strictly stationary solution, derive weak dependence and moment conditions, show consistency of the estimators, and perform a small empirical study demonstrating how our model matches with real data.

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Holdings
Item type	Current library	Call number	Vol info	Copy number	Status	Notes	Date due	Barcode
Journal Article	Main Library - Special Collections	HB139.T52 ECO (Browse shelf(Opens below))	Vol. 29, no.2 (pages 267-289)	SP17542	Not for loan	For In House Use Only

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Improvements in data acquisition and processing techniques have led to an almost continuous flow of information for financial data. High-resolution tick data are available and can be quite conveniently described by a continuous-time process. It is therefore natural to ask for possible extensions of financial time series models to a functional setup. In this paper we propose a functional version of the popular autoregressive conditional heteroskedasticity model. We will establish conditions for the existence of a strictly stationary solution, derive weak dependence and moment conditions, show consistency of the estimators, and perform a small empirical study demonstrating how our model matches with real data.

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