Modelling financial time series with SEMIFAR-GARCH model

Yuanhua Feng, Jan Beran, Yu Keming

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


A class of semiparametric fractional autoregressive models with generalized autoregressive conditional heteroskedastic (GARCH) errors, 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.

Original languageEnglish
Pages (from-to)395-412
Number of pages18
JournalIMA Journal of Management Mathematics
Issue number4
Publication statusPublished - Oct 2007


  • Asymptotic property
  • Financial time series
  • GARCH model
  • Kernel estimation
  • Parameter estimation
  • SEMIFAR model


Dive into the research topics of 'Modelling financial time series with SEMIFAR-GARCH model'. Together they form a unique fingerprint.

Cite this