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

Matteo Bonato (), Oguzhan Cepni, Rangan Gupta and Christian Pierdzioch
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Matteo Bonato: Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France

No 202316, Working Papers from University of Pretoria, Department of Economics

Abstract: 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.

Keywords: Realized volatility; Agricultural commodities; Realized moments; Sentiment; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 G41 Q10 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2023-05
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Journal Article: Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter? (2024)
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