Cooling Degree Day (CDD)

What Is a Cooling Degree Day? (CDD)

A cooling degree day (CDD) is a measurement designed to quantify the demand for energy needed to cool buildings. It is the number of degrees that a day's average temperature is above 65o Fahrenheit (18o Celsius). Studies have shown that when the outside temperature reaches that level, people inside no longer want the building heated, but instead begin to consider cooling the building.

This measure has relevance to the price of weather derivatives traded on the basis of an index made up of monthly CDD values. The settlement price for a weather futures contract is calculated by summing CDD values for a month and multiplying that sum by $20.

Understanding Cooling Degree Days (CDD)

While CDD can describe the overall need for cooling as part of the planning for residential or commercial buildings, it is critical for the pricing of weather derivatives. These instruments create a risk management tool that utility, agriculture, construction, and other firms can use to hedge their activities affected by outdoor climate, whether it be energy demand, growing season, or outdoor work time.

One thing to note is that CDD are extremely localized. Cooling needs vary greatly depending on the geographical region. Furthermore, the average CDD in one building may not have the same impact as it does on the building next door due to differences in construction, orientation relative to other buildings, insulation, sun exposure, and the nature of the building's usage.

A similar measurement, heating degree day (HDD), reflects the amount of energy needed to heat a home or business.

How to Calculate Cooling Degree Days (CDD)

There are several ways to calculate CDD. The more detailed a record of temperature data, the more accurately the CDD can be calculated. Here are examples of two ways that are often used.

A utility company might recognize that the price they are paying energy producers will be cost-prohibitive if they need to provide more energy than they expected. Using the information from the previous example, they could take weather data from previous years and current seasons to estimate their risk. Weather derivative (futures) contracts could then be purchased to protect against significant losses if the company expected higher temperatures. These same contracts could likewise be sold if the company expected significantly lower temperatures.