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Gaussian Analysis of Non-Gaussian Time Series

Author

Listed:
  • Dimitris Kugiuntzis
  • Efthimia Bora-Senta

Abstract

A framework is proposed for the analysis of non-Gaussian time series under the Gaussian assumption. The analysis is based on the Gaussian autocorrelation computed from the transform of the sample autocorrelation. It is shown that this approach improves the linear autoregressive fit. We also use it to generate time series that preserve the original autocorrelation and marginal distribution and develop a combined test that discriminates whether a linear stochastic time series is a monotonic or non-monotonic transform of a Gaussian time series. The usefulness of the proposed analysis is demonstrated on stock exchange volumes of several world markets.

Suggested Citation

  • Dimitris Kugiuntzis & Efthimia Bora-Senta, 2010. "Gaussian Analysis of Non-Gaussian Time Series," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 53(2), pages 295-322.
    • Handle: RePEc:bxr:bxrceb:2013/80945
    Note: Numéro Spécial « Special Issue on Nonlinear Financial Analysis :Editorial Introduction » Guest Editor :Catherine Kyrtsou
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    More about this item

    Keywords

    Non-Gaussian time series; Autocorrelation; Autoregressive models; Surrogate data; Hypothesis testing; International financial markets;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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