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

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.