Modelling financial time series with SEMIFAR GARCH model | Zendy

Yuanhua Feng | Zendy; Jan Beran | Zendy; Keming Yu | Zendy

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Modelling financial time series with SEMIFAR GARCH model

Author(s) -

Yuanhua Feng,

Jan Beran,

Keming Yu

Publication year - 2007

Publication title -

ima journal of management mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.484

H-Index - 34

eISSN - 1471-6798

pISSN - 1471-678X

DOI - 10.1093/imaman/dpm024

Subject(s) - autoregressive conditional heteroskedasticity , heteroscedasticity , autoregressive model , econometrics , series (stratigraphy) , range (aeronautics) , time series , finance , mathematics , computer science , economics , statistics , volatility (finance) , paleontology , materials science , composite material , biology

A class of semiparametric fractional autoregressive GARCH models (SEMIFAR-GARCH), which includes deterministic trends, difference stationarity and stationarity with short- and long-range dependence, and heteroskedastic model errors, is very powerful for modelling financial time series. This paper discusses the model fitting, including an efficient algorithm and parameter estimation of GARCH error term. So that the model can be applied in practice. We then illustrate the model and estimation methods with a few of different finance data sets.

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