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Functional generalized autoregressive conditional heteroskedasticity

Alexander Aue, Lajos Horvath and Daniel Pellatt

MPRA Paper from University Library of Munich, Germany

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

Keywords: Econometrics; Financial time series; Functional data; GARCH processes; Stationary solutions (search for similar items in EconPapers)
JEL-codes: C1 C13 C4 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2015-08-20
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Journal Article: Functional Generalized Autoregressive Conditional Heteroskedasticity (2017) Downloads
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