Modelling financial time series with SEMIFAR-GARCH model
AbstractA class of semiparametric fractional autoregressive GARCH models (SEMIFARGARCH), 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 Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 07-14.
Length: 23 pages
Date of creation: 01 Dec 2007
Date of revision:
Other versions of this item:
- Feng, Yuanhua & Beran, Jan & Yu, Keming, 2006. "Modelling financial time series with SEMIFAR-GARCH model," MPRA Paper 1593, University Library of Munich, Germany.
- 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 &bull Diffusion Processes
- 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-2008-08-06 (All new papers)
- NEP-ECM-2008-08-06 (Econometrics)
- NEP-ETS-2008-08-06 (Econometric Time Series)
- NEP-FMK-2008-08-06 (Financial Markets)
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