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Estimating VAR-MGARCH models in multiple steps

Author

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  • Carnero M. Angeles

    (Dep. Fundamentos del Análisis Económico, Universidad de Alicante, Spain)

  • Eratalay M. Hakan

    (Dep. Economics, European University at St Petersburg, Russian Federation)

Abstract

This paper analyzes the performance of multiple steps estimators of vector autoregressive multivariate conditional correlation GARCH models by means of Monte Carlo experiments. We show that if innovations are Gaussian, estimating the parameters in multiple steps is a reasonable alternative to the maximization of the full likelihood function. Our results also suggest that for the sample sizes usually encountered in financial econometrics, the differences between the volatility and correlation estimates obtained with the more efficient estimator and the multiple steps estimators are negligible. However, when innovations are distributed as a Student-t, using multiple steps estimators might not be a good idea.

Suggested Citation

  • Carnero M. Angeles & Eratalay M. Hakan, 2014. "Estimating VAR-MGARCH models in multiple steps," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 339-365, May.
    • Handle: RePEc:bpj:sndecm:v:18:y:2014:i:3:p:27:n:5
    DOI: 10.1515/snde-2012-0065
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    Cited by:

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    3. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
    4. Rick Bohte & Luca Rossini, 2019. "Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models," JRFM, MDPI, vol. 12(3), pages 1-18, September.
    5. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    6. Esposti, Roberto, 2021. "On the long-term common movement of resource and commodity prices.A methodological proposal," Resources Policy, Elsevier, vol. 72(C).
    7. Ariana Paola Cortés à ngel & Mustafa Hakan Eratalay, 2021. "Deedp Diving Into The S&P 350 Europe Index Network Ans Its Reaction To Covid-19," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 134, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    8. M. Hakan Eratalay & Evgenii Vladimirov, 2017. "Mapping the Stocks in MICEX: Who Is Central in Moscow Stock Exchange?," EUSP Department of Economics Working Paper Series Ec-01/17, European University at St. Petersburg, Department of Economics.
    9. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
    10. Esposti, Roberto, 2017. "What Makes Commodity Prices Move Together? An Answer From A Dynamic Factor Model," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 260889, European Association of Agricultural Economists.
    11. Carnero M. Angeles & Eratalay M. Hakan, 2014. "Estimating VAR-MGARCH models in multiple steps," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 339-365, May.
    12. Mustafa Hakan Eratalay & Ariana Paola Cortés Ángel, 2022. "The Impact of ESG Ratings on the Systemic Risk of European Blue-Chip Firms," JRFM, MDPI, vol. 15(4), pages 1-41, March.
    13. Hashem Zarafat & Sascha Liebhardt & Mustafa Hakan Eratalay, 2022. "Do ESG Ratings Reduce the Asymmetry Behavior in Volatility?," JRFM, MDPI, vol. 15(8), pages 1-32, July.

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    More about this item

    Keywords

    financial markets; volatility spillovers;

    JEL classification:

    • 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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