AbstractThe weather is one of the factors that may have an impact on the countries' economies. There are two main hedging ways against unexpected weather conditions: weather derivatives and weather insurances. During the last two decades, companies started to use weather derivatives against weather issues, especially in the energy and agriculture sectors. Starting from weather derivatives' first launch, their transaction volumes at the exchange and over‐the‐counter markets have increased. In addition to the increasing dependency of the economies on the weather, providing the weather derivative contracts with a reasonable premium amount is another reason which helps to have this positive trend. Since weather derivatives have similar parameters and rules with classical financial derivatives, it is possible to use the same pricing approaches for financial and weather derivatives. Monte–Carlo simulation, based on random number generation, is one of the existing methods of pricing derivative contracts. A difference between simulated values and really occurred data is the base point of the expected payoff or price of the contract. The current article introduces weather derivatives and shows two different approaches to their pricing, where one of them requires deeper statistical analysis. Adding the statistical analysis into the consideration, defining the relation between each data value, helps to provide better estimation and less volatility. Having less volatility can provide more accurate estimations and reasonable prices that are affordable and desired by the companies.This article is categorized under: Energy Systems Economics > Economics and Policy Energy Systems Economics > Systems and Infrastructure