WEATHER FORECASTING AND
ENERGY TRADING
NOTES FROM THE TRENCHES
April 15, 2010
Vincent Kaminski
Rice University
Jesse H. Jones Graduate
School of Business
© 1999 VK-9060359-1
Outline
 Energy Impact on the Commodity Markets
 Notes from the Trenches: My Interactions with Weather
Forecasters
 The Future Trends
Weather Derivatives
Algorithmic Trading
In-house Weather Modeling?
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WEATHER IMPACT ON
THE COMMODITY
MARKETS
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The Channels of Transmission
The impact of weather on the agricultural markets
The dominant transmission channel: the supply side
The impact unfolds relatively slowly over time, except for some
extreme event
Extremely cold weather in Florida affecting orange juice market
Freeze in the coffee producing regions
The potential for systemic impact due to long-term weather trends
(Pinot Noir from Scotland?)
The impact on the demand side: local and short-term
The impact of weather on the agricultural markets was
recognized in the relatively early stages of development of the
futures exchanges
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Words of Wisdom From the Past
“So much for the financial conditions.
The spring had been backward, cold, bitter, inhospitable, and Jadwin began to suspect that the
wheat crop of his native country, that for so long had been generous, and of excellent quality,
was now to prove—it seemed quite possible--scant and of poor condition. He began to watch
the weather, and to keep an eye upon the reports from the little county seats and "centres" in
the winter wheat States. These, in part, seemed to confirm his suspicions.
From Keokuk, in Iowa, came the news that winter wheat was suffering from want of moisture.
Benedict, Yates' Centre, and Douglass, in southeastern Kansas, sent in reports of dry, windy
weather that was killing the young grain in every direction, and the same conditions seemed to
prevail in the central counties. In Illinois, from Quincy and Waterloo in the west, and from
Ridgway in the south, reports came steadily to hand of freezing weather and bitter winds. All
through the lower portions of the State the snowfall during the winter had not been heavy
enough to protect the seeded grain. But the Ohio crop, it would appear, was promising enough,
as was also that of Missouri. In Indiana, however, Jadwin could guess that the hopes of even a
moderate yield were fated to be disappointed; persistent cold weather, winter
continuing almost up to the first of April, seemed to have definitely settled the question.”
Frank Norris (1870-1902), “The Pit,” New York, 1903
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The Channels of Transmission (2)
The channels of transmission in the case of energy markets:
primarily through the demand side
The impact on the supply side: primarily through specific events of
varying magnitude, both in terms of scope and duration
Hurricanes
Precipitation impact on hydro power
Special local events
The events require good understanding of local conditions
The impact varies from long-term to short-term and fairly local
Fog in the Houston Ship Channel
High waters of the coast of California
Low water level in the Rhine river
Wells freeze-offs
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The Channels of Transmission (3)
The challenge: weather forecasting for an energy trading operation
Practically everybody has the access to the same source data and
analytical skills (for a price)
The competitive advantage can be acquired through:
Better interpretation of the conflicting forecasts (when the models diverge)
Improved skills in the process of selection of the weather forecasting firms
Moving up the supply chain to beat the competition
Faster acquisition, processing, visualization, and delivery of the
weather information
Elimination of some links in the supply chain
Direct acquisition of the model results and atomic weather data
Most important: better understanding of the impact of weather on the
energy markets, contingent on other conditions (trading positions,
conditions of the energy infrastructure)
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THE EARLY DAYS
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Weather Function in Energy Trading
 Financial firms trading agricultural futures had weather
teams supporting traders
Many weather analysts made contributions to energy trading
in free time or when market conditions required their
involvement
The first energy weather group was started by Enron
The Enron’s move was quickly replicated by the rest of the
industry
The weather group was considered to be critical to the
success of Enron trading
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UNFOLDING TRENDS
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WEATHER DERIVATIVES
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Weather Derivatives: Definition
 A special version of weather insurance
Structured as derivative transactions
Regulatory considerations were the most compelling reason
Designed to hedge the risk of low severity, high probability events
By definition, actively traded on multiple platforms
A very successful take-off
The merchant energy industry used conditional probability
distributions (unlike some counterparties in the insurance industry)
Some companies exploited successfully the human island
phenomenon and data errors
Very limited repeat business
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Weather Derivatives: The Future
 My opinion: it’s not a viable business, as opposed to highly
structured, customized weather insurance
The reasons:
Weather derivatives put on head the very principle of weather
derivatives (the few will pay for the losses of the many instead
of the many paying for the losses of the few)
The high probability events represent permanent and recurring
conditions of doing business and are most effectively
addressed by incorporating the cost in the price of a product
The cash flow smoothing aspect of weather derivatives: the
premiums are effectively capped by interest rates
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In-house Modeling
Some energy trading organizations move to direct acquisition of
certain raw or intermediate data
Model outputs
Certain critical data sets
For example, sea surface temperatures
It can not be excluded that at some point some companies
will engage in direct modeling of weather and/or computational
activities
Will the market size justify this effort?
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Algorithmic Trading
Several energy trading organizations are experimenting with trading
strategies based on data mining
Search for the patterns of markets response to the weather data
Automatic execution of transactions
The challenges
The difficulty of identifying and capturing all the relevant data
The reaction to the weather data depends on many factors, which may
not be measurable
Example: positions held by other traders
The volume may not be sufficient to bother
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Contact information:
vkaminski@aol.com
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