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Multivariate dynamic mixed-frequency density pooling for financial forecasting

Author

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  • Virbickaitė, Audronė
  • Lopes, Hedibert F.
  • Zaharieva, Martina Danielova

Abstract

This article investigates the benefits of combining information available from daily and intraday data in financial return forecasting. The two data sources are combined via a density pooling approach, wherein the individual densities are represented as a copula function, and the potentially time-varying pooling weights depend on the forecasting performance of each model. The dependence structure in the daily frequency case is extracted from a standard static and dynamic conditional covariance modeling, and the high-frequency counterpart is based on a realized covariance measure. We find that incorporating both high- and low-frequency information via density pooling provides significant gains in predictive model performance over any individual model and any model combination within the same data frequency. A portfolio allocation exercise quantifies the economic gains by producing investment portfolios with the smallest variance and highest Sharpe ratio.

Suggested Citation

  • Virbickaitė, Audronė & Lopes, Hedibert F. & Zaharieva, Martina Danielova, 2025. "Multivariate dynamic mixed-frequency density pooling for financial forecasting," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1184-1198.
    • Handle: RePEc:eee:intfor:v:41:y:2025:i:3:p:1184-1198
    DOI: 10.1016/j.ijforecast.2024.11.011
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