Forecasting the Realized Volatility of Agricultural Commodity Prices: Does Sentiment Matter?

Matteo Bonato, Oguzhan Cepni, Rangan Gupta, Christian Pierdzioch

Research output: Working paperResearch


We analyze the out-of-sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high-frequency intra-day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside (``good”) volatility, realized downside (``bad”) volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection, and, (ii) a model-based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR-RV model and the HAR-RV-sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment.
Original languageEnglish
Place of PublicationPretoria
PublisherUniversity of Pretoria
Number of pages43
Publication statusPublished - 2023
SeriesWorking Paper Series / Department of Economics. University of Pretoria


  • Realized volatility
  • Agricultural commodities
  • Realized moments
  • Sentiment
  • Forecasting

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