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Sparse Bayesian time-varying covariance estimation in many dimensions

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  • Kastner, Gregor

Abstract

We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global–local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero. To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as to daily log-returns of 300 S&P 500 members. Our approach yields precise correlation estimates, strong implied minimum variance portfolio performance and superior forecasting accuracy in terms of log predictive scores when compared to typical benchmarks.

Suggested Citation

  • Kastner, Gregor, 2019. "Sparse Bayesian time-varying covariance estimation in many dimensions," Journal of Econometrics, Elsevier, vol. 210(1), pages 98-115.
  • Handle: RePEc:eee:econom:v:210:y:2019:i:1:p:98-115
    DOI: 10.1016/j.jeconom.2018.11.007
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    Citations

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    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2019. "Large-scale portfolio allocation under transaction costs and model uncertainty," Journal of Econometrics, Elsevier, vol. 212(1), pages 221-240.
    2. Gregor Zens, 2018. "Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership," Papers 1809.04853, arXiv.org, revised Jan 2019.
    3. Moura, Guilherme V. & Ruiz, Esther & Santos, André A. P., 2019. "Comparing Forecasts of Extremely Large Conditional Covariance Matrices," DES - Working Papers. Statistics and Econometrics. WS 29291, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. repec:gam:jecnmx:v:7:y:2019:i:3:p:29-:d:243958 is not listed on IDEAS
    5. 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.
    6. Emanuela Ciapanna & Marco Taboga, 2019. "Bayesian Analysis of Coefficient Instability in Dynamic Regressions," Econometrics, MDPI, Open Access Journal, vol. 7(3), pages 1-32, June.
    7. Niko Hauzenberger & Michael Pfarrhofer, 2019. "Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy," Papers 1911.06206, arXiv.org.
    8. Pfarrhofer, Michael & Niko , Hauzenberger, 2019. "Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy," Working Papers in Economics 2019-6, University of Salzburg.
    9. Michael Pfarrhofer & Philipp Piribauer, 2018. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models," Papers 1805.10822, arXiv.org.

    More about this item

    Keywords

    Dynamic correlation; Factor stochastic volatility; Curse of dimensionality; Shrinkage; Minimum variance portfolio;

    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
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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