Modelling financial time series with SEMIFAR-GARCH model
AbstractA 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 1593.
Date of creation: 2006
Date of revision:
Financial time series; GARCH model; SEMIFAR model; parameter estimation; kernel estimation; asymptotic property;
Other versions of this item:
- Yuanhua Feng & Jan Beran & Keming Yu, 2007. "Modelling financial time series with SEMIFAR-GARCH model," CoFE Discussion Paper 07-14, Center of Finance and Econometrics, University of Konstanz.
- G00 - Financial Economics - - General - - - General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-02-10 (All new papers)
- NEP-ECM-2007-02-10 (Econometrics)
- NEP-ETS-2007-02-10 (Econometric Time Series)
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