Pricing temperature-based weather derivatives in China

Purpose–The purpose of this paper is to propose a feasible model for the daily average temperatures of Beijing, Shanghai and Shenzhen, in order to price temperature-based weather derivatives; also to derive analytical approximation formulas for the sensitivities of these contracts. Design/methodology/approach–This study proposes a seasonal volatility model that estimates daily average temperatures of Beijing, Shanghai and Shenzhen using the mean-reverting Ornstein-Uhlenbeck process. It then uses the analytical approximation and Monte Carlo methods to price heating degree days and cooling degree days options for these cities. In addition, it derives and calculates the option sensitivities on the basis of an analytical approximation formula. Findings–There exists a strong seasonality in the volatility of daily average temperatures of Beijing, Shanghai and Shenzhen. To model the seasonality Fourier approximation is applied to the squared volatility of daily temperatures. The analytical approximation formulas and Monte Carlo simulation produce very similar prices for heating/cooling degree days options in Beijing and Shanghai, a result that also verifies the convergence of the Monte Carlo and approximation estimators. However, the two methods do not produce converging option prices in the case of HDD options for Shenzhen. Originality/value–The article provides important insight to investors and hedgers by proposing a feasible model for pricing temperature-based weather contracts in China and derives analytical approximations for the sensitivities of heating/cooling degree days options.