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Large-Dimensional Portfolio Selection with a High-Frequency-Based Dynamic Factor Model

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  • Simon T Bodilsen

Abstract

This article proposes a new predictive model for large-dimensional realized covariance matrices. Using high-frequency data, we estimate daily realized covariance matrices for the constituents of the S&P 500 Index and a set of observable factors. Using a standard decomposition of the joint covariance matrix, we express the covariance matrix of the individual assets similar to a dynamic factor model. To forecast the covariance matrix, we model the components of the covariance structure using a series of autoregressive processes. A novel feature of the model is the use of the data-driven hierarchical clustering algorithm to determine the structure of the idiosyncratic covariance matrix. A simulation study shows that this method can accurately estimate the block structure as long as the number of blocks is small relative to the number of stocks. In an out-of-sample portfolio selection exercise, we find that the proposed model outperforms other commonly used multivariate volatility models in extant literature.

Suggested Citation

  • Simon T Bodilsen, 2025. "Large-Dimensional Portfolio Selection with a High-Frequency-Based Dynamic Factor Model," Journal of Financial Econometrics, Oxford University Press, vol. 23(2), pages 384-399.
  • Handle: RePEc:oup:jfinec:v:23:y:2025:i:2:p:384-399.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbae018
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    More about this item

    Keywords

    big data; hierarchical clustering; high-frequency data; minimum variance portfolio; multivariate volatility;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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