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Abstract

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.



Item Type: MPRA Paper -

Institution: Heriot-Watt University, University of Konstanz and Brunel University-

Original Title: Modelling financial time series with SEMIFAR-GARCH model-

Language: English-

Keywords: Financial time series; GARCH model; SEMIFAR model; parameter estimation; kernel estimation; asymptotic property-

Subjects: G - Financial Economics > G0 - General > G00 - GeneralC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion ProcessesC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General-





Author: Feng, Yuanhua

Source: https://mpra.ub.uni-muenchen.de/1593/







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