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A generalized VECM/VAR-DCC/ADCC framework and its application in the Black-Litterman model

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  • Qi Deng

Abstract

Purpose - The existing literature on the Black-Litterman (BL) model does not offer adequate guidance on how to generate investors’ views in an objective manner. Therefore, the purpose of this paper is to establish a generalized multivariate Vector Error Correction Model (VECM)/Vector Auto-Regressive (VAR)-Dynamic Conditional Correlation (DCC)/Asymmetric DCC (ADCC) framework, and applies it to generate objective views to improve the practicality of the BL model. Design/methodology/approach - This paper establishes a generalized VECM/VAR-DCC/ADCC framework that can be utilized to model multivariate financial time series in general, and produce objective views as inputs to the BL model in particular. To test the VECM/VAR-DCC/ADCC preconditioned BL model’s practical utility, it is applied to a six-asset China portfolio (including one risk-free asset). Findings - With dynamically optimized view confidence parameters, the VECM/VAR-DCC/ADCC preconditioned BL model offers clear advantage over the standard mean-variance method, and provides an automated portfolio optimization alternative to the classic BL approach. Originality/value - The VECM/VAR-DCC/ADCC framework and its application in the BL model proposed by this paper provide an alternative approach to the classic BL method. Since all the view parameters, including estimated mean return vectors, conditional covariance matrices and pick matrices, are generated in the VECM/VAR and DCC/ADCC preconditioning stage, the model improves the objectiveness of the inputs to the BL stage. In conclusion, the proposed model offers a practical choice for automated portfolio balancing and optimization in a China context.

Suggested Citation

  • Qi Deng, 2018. "A generalized VECM/VAR-DCC/ADCC framework and its application in the Black-Litterman model," China Finance Review International, Emerald Group Publishing Limited, vol. 8(4), pages 453-467, March.
  • Handle: RePEc:eme:cfripp:cfri-07-2016-0095
    DOI: 10.1108/CFRI-07-2016-0095
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    Citations

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    Cited by:

    1. Wen, Danyan & Wang, Yudong & Ma, Chaoqun & Zhang, Yaojie, 2020. "Information transmission between gold and financial assets: Mean, volatility, or risk spillovers?," Resources Policy, Elsevier, vol. 69(C).
    2. 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.

    More about this item

    Keywords

    VAR; VECM; Portfolio optimization; DCC; ADCC; Black-Litterman (BL); C32; C53; G11;
    All these keywords.

    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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