An Empirical Study On The Pricing Of A Class Of Weather Derivatives

The “red alarm bell” of the global climate crisis has been sounded,with extreme weather having a significant negative impact on agriculture around the world.At the same time,weather derivatives as an important weather risk management tool,the international weather derivatives market is growing.Frequent occurrence of climate change and extreme weather events makes weather risk management more and more important.Therefore,it is urgent for our country to introduce weather derivatives to hedge the risk of meteorological disasters and further protect the income of farmers and enterprises.At this time,the pricing research of weather derivatives has more important significance.This paper first systematically introduced the basic knowledge of weather risk management and weather derivatives.Then,starting from the impact of different meteorological disasters on crop yields in Heilongjiang Province,the grey correlation analysis method was used to obtain the grey correlation degree and ranking of different meteorological disasters and main crop yields,and the conclusion was drawn: Drought is the meteorological disaster that affects the crop yield most in Heilongjiang Province.In order to quantify the internal relationship between the impact degrees of different meteorological disasters,VAR model was used in this paper to study the impact of different meteorological disasters on soybean yield,taking soybean as an example.According to the impulse response graph and variance analysis table in VAR model,temperature is the biggest weather factor affecting soybean yield.In order to construct the temperature prediction model,this paper selects the daily mean temperature of Heihe City,Heilongjiang Province from 1961 to 2020,introduces linear trend and random fluctuation into the classical harmonic oscillator model,deduces the random harmonic oscillator model of temperature,and estimates the unknown parameters in the model,and obtains the final expression of the model.The average daily temperature of four different periods is simulated and compared with the historical real temperature,and the fitting effect is better.Finally,combined with Monte Carlo method,the January and August 2020European-style call options of Heihe City,Heilongjiang Province are priced.The simulation value of option price is obtained based on different base temperature,and the error is calculated by comparing with the actual value.The results show that this method is effective in simulating pricing.