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.
Description:
DATA AVAILABILITY STATEMENT :
The data that support the findings of this study are
available from Refinitiv Eikon. Restrictions apply to the
availability of these data, which were used under license
for this study. Data are available from the author(s) with
the permission of Refinitiv Eikon.