Multivariate dynamic mixed-frequency density pooling for financial forecasting

Audronė Virbickaitė, Hedibert F. Lopes and Martina Danielova Zaharieva

International Journal of Forecasting, 2025, vol. 41, issue 3, 1184-1198

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.

Keywords: Conditional value at risk; Density combination; High frequency; Realized volatility; Global minimum variance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:41:y:2025:i:3:p:1184-1198

DOI: 10.1016/j.ijforecast.2024.11.011

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