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Non-stationarities in financial time series, the long range dependence and the IGARCH effects

Author

Listed:
  • Thomas Mikosch

    (Dept. Actuarial Mathematics, University of Copenhagen)

  • Catalin Starica

    (Dept. Mathematical Statistics & Economics, Gothenburg University & CTH)

Abstract

In this paper we give the theoretical basis of a possible explanation for two stylized facts observed in long log-return series: the long range dependence (LRD) in volatility and the integrated GARCH (IGARCH). Both these effects can be theoretically explained if one assumes that the data is non-stationary (changing unconditional variance).

Suggested Citation

  • Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0412005
    Note: Type of Document - pdf; pages: 19
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0412/0412005.pdf
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    More about this item

    Keywords

    Sample ACF; Garch process; long range dependence; IGARCH; non- stationarities; time-varying unconditional variance;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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