A New Generation of Cover Against Extreme Cold in Texas

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The rising intensity of natural catastrophes around the world is increasing pressure on the prices and
capacities of corporate insurance programs. In response, more corporate and public sector clients are turning to parametric insurance to cover their climate and emerging risk exposures.

analog thermometer covered in snow

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Looking back at the recent powerful cold waves that swept across North America – Winter Storm Uri (2021) & Winter Storm Elliott (2022) – energy producers across Texas are struggling to mitigate their financial exposure to extreme cold events

February 11-20, 2021: While The Great Texas Freeze, also known as Winter Storm Uri, was not the most severe cold snap in the state’s recorded history, it still caused a billion dollars worth of damages, significant fatalities, and state-wide blackouts that took a substantial financial toll on Texas residents and wholesale energy producers. A freezing event like the Great Texas Freeze was believed to be a rarity, yet Winter Storm Elliott (2022) followed just a year later.

How climate change is affecting winter storms in Texas

The Great Texas Freeze began at the beginning of February, when the air in the atmosphere above the Arctic warmed suddenly, causing a weakening of the polar vortex, a collection of winds that keeps cold air at the North Pole. This phenomenon allowed these cold winds to spill into the temperate regions of Asia, Europe and North America.

One key factor of the brutal cold that surrounded the state of Texas was the high negative Arctic Oscillation (AO), a back-and-forth shifting of atmospheric pressure between the Arctic and the North Pacific and North Atlantic mid-latitudes. The AO influences weather and climate in North America, Europe, and Asia, making the most impact during winter. A high positive AO can affect the powerful mid-latitude jet stream to move storms northward, reducing cold air blasts in the mid-latitude regions. A weaker jet can dip further south with a negative AO, enabling Arctic air outbreaks into the mid-latitude regions.

Another element at play in the cold air outbreak was the polar vortex. A polar vortex extensively covers low-pressure and cold air surrounding Earth’s poles. When the polar vortex is strong and stable, the polar jet stream shifts northward, causing the cold air to remain in the Arctic. When the vortex weakens or is disrupted, the jet stream often becomes extremely wavy, allowing warm air to flood into the Arctic and polar air to sink into the mid-latitudes.

During the worst of the storm, 157 million people across North America were living under extreme winter storm warnings or weather advisories. Due to the power supply damage, the cold highlighted the need for grid modernization, particularly in Texas.

Why energy producers in Texas are highly vulnerable to extreme weather events

Texas is the only state in the mainland United States with a fully independent electrical grid, meaning it does not connect to other power grids across the country. As a result, the energy supply across the state is less regulated and more isolated. 

Consequently, 48 percent of the power generation capacity was completely offline when the Great Freeze hit Texas. Electricity demand soon exceeded supply, where energy companies that buy their energy wholesale – as it’s highly affordable due to the deregulated, market-based system – suddenly saw a significant price increase in wholesale energy. Many energy producers had to pay out of pocket without insurance, inevitably taking a massive hit to their revenue.

Traditional insurance solutions depend on physical damage

Most property insurance policies require “physical loss or damage” to the insured property as a condition of coverage. For most property damage claims, this condition is clearly met if the insured property is damaged by water, snow, ice or wind.

However, several events, such as extreme cold and subsequent power and utilities interruptions, may not cause direct physical losses but can still significantly negatively impact earnings, exposing financial and operational risks.

While such events can cause massive damages, they tend to be complicated or expensive to mitigate and are often not covered by traditional indemnity insurance solutions.

Parametric insurance: an alternative insurance solution against winter storm

Through extensive use of data for pricing, parametric insurance radically simplifies the process, from underwriting to claims handling. The transparent, objective and non-ambiguous nature of a parametric trigger removes the need for any traditional loss adjustment process and, in general, increases the efficiency and effectiveness of the insurance program.

Perhaps the most significant benefit of parametric insurance in the wake of extreme cold weather, as experienced in February 2021 and December 2022  in the US, is that it is not based on material damage alone. Parametric policies cover revenue losses regardless of whether physical damage occurred or not, thus protecting against the bulk of financial losses arising from wholesale energy price spikes, power cuts and infrastructure shutdowns.

case study example

Client case study

An energy company in Texas needed to hedge against the significant price increase of wholesale energy following an extreme - and rare - cold event.


The client had endured both Winter Storm Uri (2021) & Winter Storm Elliott (2022), where the wholesale energy cost was astronomical.

This severely impacted their balance sheet, as they were forced to pay out of pocket to cover the unprecedented price increase.


Descartes was able to deliver a tailor-made solution for extreme cold, where if the temperature hit a low predetermined point, the client would receive the corresponding payout, providing a hedge against revenue instability. 

The client preferred a parametric solution over other financial solutions as it not only ensured tax-free payouts, but also Descartes could accurately price an extreme cold event due to their historical experiences, enabling a more accurate pricing structure of the unique risk.

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