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Evaluating portfolio value-at-risk using semi-parametric GARCH models

Author

Listed:
  • ROMBOUTS, Jeroen VK
  • VERBEEK, Marno

Abstract

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations. Finally, we examine the economic value of the multivariate GARCH models by determining optimal portfolios based on maximizing expected returns subject to a VaR constraint, over a period of 500 consecutive days. Again, the superiority and robustness of the semi-parametric model is confirmed.
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Suggested Citation

  • ROMBOUTS, Jeroen VK & VERBEEK, Marno, 2009. "Evaluating portfolio value-at-risk using semi-parametric GARCH models," LIDAM Reprints CORE 2299, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:2299
    DOI: 10.1080/14697680902785284
    Note: In : Quantitative Finance, 9(6), 737-745, 2009
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    Cited by:

    1. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
    2. Francq, Christian & Zakoïan, Jean-Michel, 2020. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Journal of Econometrics, Elsevier, vol. 217(2), pages 356-380.
    3. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, vol. 5(4), pages 1-26, April.
    4. Pei Pei, 2010. "Backtesting Portfolio Value-at-Risk with Estimated Portfolio Weights," Caepr Working Papers 2010-010, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    5. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    6. Moralles, Herick Fernando & do Nascimento Rebelatto, Daisy Aparecida, 2016. "The effects and time lags of R&D spillovers in Brazil," Technology in Society, Elsevier, vol. 47(C), pages 148-155.
    7. Cristi Spulbar & Ramona Birau & Iqbal Thonse Hawaldar & Jatin Trivedi & Anca Ioana Iacob (Troto), 2023. "Measuring Asymmetric Volatility Of Uk, France, And German Stock Markets," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 134-146, February.
    8. Lumengo BONGA-BONGA & Lebogang NLEYA, 2018. "Assessing Portfolio Market Risk in the BRICS Economies: Use of Multivariate GARCH Models," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 71(2), pages 87-128.
    9. Wei Kuang, 2024. "High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-35, May.
    10. He, Kaijian & Wang, Lijun & Zou, Yingchao & Lai, Kin Keung, 2014. "Value at risk estimation with entropy-based wavelet analysis in exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 62-71.
    11. Kun Zhang & Laiwan Chan, 2009. "Efficient factor GARCH models and factor-DCC models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 71-91.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G3 - Financial Economics - - Corporate Finance and Governance
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics

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