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Multivariate GARCH models and the Black-Litterman approach for tracking error constrained portfolios: an empirical analysis

Author

Listed:
  • Giulio Palomba

Abstract

In a typical tactical asset allocation setup, managers generally make their choices with the aim of beating a benchmark portfolio. In this context, the pure Markowitz (1959) strategy does not take two aspects into account: asset returns often show changes in volatility and managers' decisions depend on private information. This paper provides an empirical model for large-scale tactical asset allocation with multivariate GARCH estimates, given a tracking error constraint. Moreover, the Black and Litterman (1991) approach makes it possible to tactically manage the selected portfolio by combining information taken from the time-varying volatility model with some personal 'views' about asset returns.

Suggested Citation

  • Giulio Palomba, 2008. "Multivariate GARCH models and the Black-Litterman approach for tracking error constrained portfolios: an empirical analysis," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 10(4), pages 379-413.
    • Handle: RePEc:ids:gbusec:v:10:y:2008:i:4:p:379-413
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    Cited by:

    1. Luca Riccetti, 2012. "Using tracking error volatility to check active management and fee level of investment funds," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 14(3), pages 139-158.
    2. Luca RICCETTI, 2010. "Minimum Tracking Error Volatility," Working Papers 340, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Ugo FRATESI, 2010. "The National and International Effects;of Regional Policy Choices: Agglomeration Economies, Peripherality and Territorial Characteristics," Working Papers 344, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    4. Harris, Richard D.F. & Stoja, Evarist & Tan, Linzhi, 2017. "The dynamic Black–Litterman approach to asset allocation," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1085-1096.
    5. Sahamkhadam, Maziar & Stephan, Andreas & Östermark, Ralf, 2022. "Copula-based Black–Litterman portfolio optimization," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1055-1070.
    6. Palomba, Giulio & Riccetti, Luca, 2012. "Portfolio frontiers with restrictions to tracking error volatility and value at risk," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2604-2615.
    7. Thomas Y. L. Lin & Jerry Yao-Chieh Hu & Paul W. Chiou & Peter Lin, 2025. "Latent Variable Estimation in Bayesian Black-Litterman Models," Papers 2505.02185, arXiv.org.
    8. Fabio FIORILLO & Agnese SACCHI, 2010. "I Want to Free-ride. An Opportunistic View on Decentralization Versus Centralization Problem," Working Papers 346, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    9. Javier Orlando Pantoja Robayo & Julián Alberto Alemán Muñoz & Diego F. Tellez-Falla, 2025. "Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 301-322, July.
    10. Anna Czapkiewicz & Artur Machno, 2013. "Empirical Verification of World’s Regions Profitability in Dynamic International Investment Strategy," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 13, pages 145-162.
    11. Andi Duqi & Leonardo Franci & Giuseppe Torluccio, 2014. "The Black-Litterman model: the definition of views based on volatility forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 24(19), pages 1285-1296, October.

    More about this item

    Keywords

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    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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