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Wake me up before you GO-GARCH

Author

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  • H. Peter Boswijk

    (Universiteit van Amsterdam)

  • Roy van der Weide

    (World Bank)

Abstract

In this paper we present a new three-step approach to the estimation of Generalized Orthogonal GARCH (GO-GARCH) models, as proposed by van der Weide (2002). The approach only requires (non-linear) least-squares methods in combination with univariate GARCH estimation, and as such is computationally attractive, especially in larger-dimensional systems, where a full likelihood optimization is often infeasible. The effectiveness of the method is investigated using Monte Carlo simulations as well as a number of empirical applications.

Suggested Citation

  • H. Peter Boswijk & Roy van der Weide, 2006. "Wake me up before you GO-GARCH," Tinbergen Institute Discussion Papers 06-079/4, Tinbergen Institute, revised 21 Sep 2006.
  • Handle: RePEc:tin:wpaper:20060079
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    References listed on IDEAS

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    1. Weide, R. van der, 2002. "Generalized Orthogonal GARCH. A Multivariate GARCH model," CeNDEF Working Papers 02-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
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    5. Tim Bollerslev, 1988. "On The Correlation Structure For The Generalized Autoregressive Conditional Heteroskedastic Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(2), pages 121-131, March.
    6. Lanne, Markku & Saikkonen, Pentti, 2007. "A Multivariate Generalized Orthogonal Factor GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 61-75, January.
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    8. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
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    2. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    3. Płuciennik Piotr, 2012. "Influence of the American Financial Market on Other Markets During the Subprime Crisis," Folia Oeconomica Stetinensia, Sciendo, vol. 12(2), pages 19-30, December.
    4. Hafner, Christian M. & Linton, Oliver, 2010. "Efficient estimation of a multivariate multiplicative volatility model," Journal of Econometrics, Elsevier, vol. 159(1), pages 55-73, November.
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    6. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series," LEM Papers Series 2006/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. Xin Zhang & Drew Creal & Siem Jan Koopman & Andre Lucas, 2011. "Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails," Tinbergen Institute Discussion Papers 11-078/2/DSF22, Tinbergen Institute.
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    14. Abdul Aziz, Nor Syahilla & Vrontos, Spyridon & M. Hasim, Haslifah, 2019. "Evaluation of multivariate GARCH models in an optimal asset allocation framework," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 568-596.
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    More about this item

    Keywords

    Multivariate GARCH; Non-Linear Least-Squares; Maximum Likelihood;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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