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Exponential smoothing of realized portfolio weights

Author

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  • Golosnoy, Vasyl
  • Gribisch, Bastian
  • Seifert, Miriam Isabel

Abstract

The model-free exponential smoothing (ES) approach is a simple and robust way to make forecasts of random vectors. In this paper we investigate ES predictors for weights of high-dimensional realized global minimum variance portfolio (GMVP) which depend only on a realized covariance matrix of financial risky assets. We contrast a direct ES prediction of realized GMVP proportions and an indirect ES forecast, where smoothing is applied to realized covariance matrices and the GMVP composition is computed afterwards. We show analytically that either direct or indirect ES predictors of GMVP proportions could be advantageous but neither of them dominates. For this reason we suggest a dynamic time series approach in order to combine them. We illustrate our findings in an empirical study for GMVPs based on 100 risky assets and report that the proposed ES forecast combination is suitable for GMVP prediction.

Suggested Citation

  • Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    • Handle: RePEc:eee:empfin:v:53:y:2019:i:c:p:222-237
    DOI: 10.1016/j.jempfin.2019.07.006
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    Cited by:

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    2. Taras Bodnar & Nestor Parolya & Erik Thorsen, 2021. "Dynamic Shrinkage Estimation of the High-Dimensional Minimum-Variance Portfolio," Papers 2106.02131, arXiv.org, revised Nov 2021.
    3. Taras Bodnar & Mathias Lindholm & Erik Thorsén & Joanna Tyrcha, 2021. "Quantile-based optimal portfolio selection," Computational Management Science, Springer, vol. 18(3), pages 299-324, July.
    4. Vera Ivanyuk, 2021. "Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation," Economies, MDPI, vol. 9(3), pages 1-19, June.
    5. Vogler, Jan & Golosnoy, Vasyl, 2023. "Unrestricted maximum likelihood estimation of multivariate realized volatility models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1063-1074.
    6. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    7. Holger Dette & Vasyl Golosnoy & Janosch Kellermann, 2023. "The effect of intraday periodicity on realized volatility measures," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(3), pages 315-342, April.
    8. Clements, Adam & Vasnev, Andrey L., 2023. "Combining simple multivariate HAR-like models for portfolio construction," Working Papers BAWP-2023-03, University of Sydney Business School, Discipline of Business Analytics.
    9. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.

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    More about this item

    Keywords

    Forecast combination; Minimum variance portfolio; Realized covariance matrix; Variance change;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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