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Modeling Conditional Covariances With Economic Information Instruments

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  • H. J. Turtle
  • Kainan Wang

Abstract

We propose a new model for conditional covariances based on predetermined idiosyncratic shocks as well as macroeconomic and own information instruments. The specification ensures positive definiteness by construction, is unique within the class of linear functions for our covariance decomposition, and yields a simple yet rich model of covariances. We introduce a property, invariance to variate order , that assures estimation is not impacted by a simple reordering of the variates in the system. Simulation results using realized covariances show smaller mean absolute errors (MAE) and root mean square errors (RMSE) for every element of the covariance matrix relative to a comparably specified BEKK model with own information instruments. We also find a smaller mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) for the entire covariance matrix. Supplementary materials for practitioners as well as all Matlab code used in the article are available online.

Suggested Citation

  • H. J. Turtle & Kainan Wang, 2014. "Modeling Conditional Covariances With Economic Information Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 217-236, April.
    • Handle: RePEc:taf:jnlbes:v:32:y:2014:i:2:p:217-236
    DOI: 10.1080/07350015.2013.859078
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    1. Turtle, H.J. & Wang, Kainan, 2016. "The benefits of improved covariance estimation," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 233-246.

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