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Modelling multivariate volatilities via conditionally uncorrelated components

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  • Fan, Jianqing
  • Wang, Mingjin
  • Yao, Qiwei

Abstract

We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem into several lower dimensional subproblems. Consistency for the estimated CUCs has been established. A bootstrap method is proposed for testing the existence of CUCs. The methodology proposed is illustrated with both simulated and real data sets.

Suggested Citation

  • Fan, Jianqing & Wang, Mingjin & Yao, Qiwei, 2008. "Modelling multivariate volatilities via conditionally uncorrelated components," LSE Research Online Documents on Economics 22875, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:22875
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    Cited by:

    1. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    2. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
    3. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
    4. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
    5. Broda, Simon A. & Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Stable mixture GARCH models," Journal of Econometrics, Elsevier, vol. 172(2), pages 292-306.
    6. Darolles, Serge & Francq, Christian & Laurent, Sébastien, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Journal of Econometrics, Elsevier, vol. 204(2), pages 223-247.
    7. García-Ferrer, Antonio & González-Prieto, Ester, 2008. "A multivariate generalized independent factor GARCH model with an application to financial stock returns," DES - Working Papers. Statistics and Econometrics. WS ws087528, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    9. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
    10. Zhang, Yongli & Rolling, Craig & Yang, Yuhong, 2021. "Estimating and forecasting dynamic correlation matrices: A nonlinear common factor approach," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    11. Morana, Claudio, 2019. "Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices," Econometrics and Statistics, Elsevier, vol. 12(C), pages 42-65.
    12. Li, Weiming & Gao, Jing & Li, Kunpeng & Yao, Qiwei, 2016. "Modelling multivariate volatilities via latent common factors," LSE Research Online Documents on Economics 68121, London School of Economics and Political Science, LSE Library.
    13. Gian Piero Aielli & Massimiliano Caporin, 2015. "Dynamic Principal Components: a New Class of Multivariate GARCH Models," "Marco Fanno" Working Papers 0193, Dipartimento di Scienze Economiche "Marco Fanno".
    14. Hetland, Simon & Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2023. "Dynamic conditional eigenvalue GARCH," Journal of Econometrics, Elsevier, vol. 237(2).
    15. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Journal of Econometrics, Elsevier, vol. 179(1), pages 16-30.
    16. Chrétien, Stéphane & Ortega, Juan-Pablo, 2014. "Multivariate GARCH estimation via a Bregman-proximal trust-region method," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 210-236.
    17. Peter Boswijk, H. & van der Weide, Roy, 2011. "Method of moments estimation of GO-GARCH models," Journal of Econometrics, Elsevier, vol. 163(1), pages 118-126, July.
    18. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
    19. Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.
    20. Simon Hetland, 2020. "Spectral Targeting Estimation of $\lambda$-GARCH models," Papers 2007.02588, arXiv.org.
    21. Naimoli, Antonio & Gerlach, Richard & Storti, Giuseppe, 2022. "Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators," Economic Modelling, Elsevier, vol. 107(C).
    22. Claudio, Morana, 2015. "Semiparametric Estimation of Multivariate GARCH Models," Working Papers 317, University of Milano-Bicocca, Department of Economics, revised 10 Dec 2015.
    23. García-Ferrer, Antonio & González-Prieto, Ester & Peña, Daniel, 2012. "A conditionally heteroskedastic independent factor model with an application to financial stock returns," International Journal of Forecasting, Elsevier, vol. 28(1), pages 70-93.
    24. Dibooglu, Sel & Cevik, Emrah I. & Gillman, Max, 2022. "Gold, silver, and the US dollar as harbingers of financial calm and distress," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 200-210.

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    More about this item

    Keywords

    bootstrap test; causality in variance; dimension reduction; extended GARCH(1; 1) model; financial returns; portfolio volatility; quasi-maximum-likelihood estimator; time series;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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