Functional Generalized Autoregressive Conditional Heteroskedasticity. (27th March 2016)

• Record Type: Journal Article
• Authors: Aue, Alexander; Horváth, Lajos; F. Pellatt, Daniel
• Is Part Of: Journal of time series analysis. Volume 38:Number 1(2017:Jan.)
• Page Start: 3
• Page End: 21
• Publication Date: 2016-03-27
• Subjects: econometrics -- financial time series -- functional data -- GARCH processes -- stationary solutions.MSC 2010: Primary: 62M10 -- Secondary: 62P20 -- 91B84; Time-series analysis -- Periodicals; 519.232
• DOI: 10.1111/jtsa.12192
• Languages: English
• ISSNs: 0143-9782

Abstract

Abstract : Heteroskedasticity is a common feature of financial time series and is commonly addressed in the model building process through the use of autoregressive conditional heteroskedastic and generalized autoregressive conditional heteroskedastic (GARCH) processes. More recently, multivariate variants of these processes have been the focus of research with attention given to methods seeking an efficient and economic estimation of a large number of model parameters. Because of the need for estimation of many parameters, however, these models may not be suitable for modelling now prevalent high‐frequency volatility data. One potentially useful way to bypass these issues is to take a functional approach. In this article, theory is developed for a new functional version of the GARCH process, termed fGARCH. The main results are concerned with the structure of the fGARCH(1, 1) process, providing criteria for the existence of strictly stationary solutions both in the space of square‐integrable and continuous functions. An estimation procedure is introduced, and its consistency and asymptotic normality are verified. A small empirical study highlights potential applications to intraday volatility estimation.