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Improved density forecasts using mixed frequency data:A Bayesian approach
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DOI：10.1016/j.jeconom.2026.106257.png)
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Abstract
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We construct a modeling framework that allows for the flexible contribution of intraday information to predict the conditional distribution of asset returns. A flexible spline specification is proposed to capture the contribution of intraday returns inside GARCH and stochastic volatility specifications for daily returns. The “focused Bayesian” prediction framework is adopted to allow the intraday contribution to the simpler GARCH specification to be trained according to the region of the support that is of interest. An empirical analysis of three broad market indices and ten industrial indices demonstrates the gains from using (i) high-frequency data, (ii) incorporated flexibly into the low-frequency model, and (iii) simpler specifications estimated using a focused Bayes approach.
Keywords:
intraday information
GARCH
stochastic volatility
mixed frequency data
focused Bayesian approach
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