Abstract:
We examine the predictive value of El Niño and La Niña weather episodes for
the subsequent realized variance of 16 agricultural commodity prices. To this
end, we use high-frequency data covering the period from 2009 to 2020 to
estimate the realized variance along realized skewness, realized kurtosis,
realized jumps, and realized upside and downside tail risks as control variables.
Accounting for the impact of the control variables as well as spillover
effects from the realized variances of the other agricultural commodities in our
sample, we estimate an extended heterogeneous autoregressive (HAR) model
by means of random forests to capture in a purely data-driven way potentially
nonlinear links between El Niño and La Niña and the subsequent realized
variance. We document such nonlinear links, and that El Niño and La Niña
increase forecast accuracy, especially at longer forecast horizons, for several of
the agricultural commodities that we study in this research.
