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The influence of global climate
variability on the prices of nine
different commodities as noted at the
New York commodity exchange was
investigated. The combination of pattern
recognition methods and multivariate
linear and nonlinear modeling yields
statistically significant relations
between global climate variability and
commodity price volatility.
Large-scale sea surface temperatures in
the Pacific and the Atlantic have
significant impact on the prices of
soybean, soybean oil, soybean meal, and
to a lesser degree on wheat and corn. Up
to 60% of the price variations of some
commodities are a direct consequence of
climate variations. The found relations
are robust and are strong enough to
yield extremely valuable information.
They are useful to analyze and judge the
price of a range of commodities. In
combination with seasonal climate
forecasting they may be exploited to
yield price forecasts on lead times
between three and six months.
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