Pricing Agricultural Revenue Insurance Based On Option Pricing Theory

In the process of agricultural production and management,yield risk and price risk are the two major risk factors.In essence,the traditional policy based agricultural insurance in China only protects yield risk,ignoring price risk.The emerging agricultural income insurance can cover the output risk and price risk of agricultural products at the same time.How to price various income insurance contracts is a relatively new topic.The core problem of the design income insurance contract is to determine the guarantee price that can be recognized by both parties from the interest of both parties.Compared with spot price,futures price is more suitable to be set as guarantee price because of its characteristics of power,predictability and continuous authenticity.At present,there are two successful projects of corn "income insurance+futures market"in China,and both of them are supported by Dalian Commodity Exchange and local government,with a total compensation amount of more than 2.02 million yuan.Based on these two cases,this paper designs and prices three kinds of future price insurance based on the corn income insurance in Shandong Province.In this paper,the classical option theory is applied to the design of pricing insurance contract.Firstly,the equilibrium method is used to guide the asset option pricing model of leek trading,which can price various other exotic options.Then,the random variables required by the yield data and price data of Maize in Shandong Province from 2000 to 2018 are selected for testing and modeling,and the unit root test and walk with the wind test are carried out for the variables.The geometric Brownian mean recovery model and ARMA model are used to model the corresponding sequence.Finally,the premium of each contract is calculated by using the closed form solution of the option pricing model of non trading assets.The analysis of hedging effect shows that the customized income insurance contract is cheaper than the exchange trading combination,and the income insurance contract is more effective than the policy insurance and price guarantee insurance.The last chapter summarizes the limitations of this study,and puts forward the corresponding suggestions and methods,hoping to give some help to further exploration:(1)volatility is generally considered to be random,which can be added to the pricing model.If the volatility follows random walk,it can be assumed that it follows geometric Brownian motion;if the volatility is an autocorrelation process,then GARCH model can also be used to simulate the stochastic volatility.(2)When dealing with correlation,the correlation is obviously unstable.We can use GARCH model to model random correlation,or we can try to use cointegration model.(3)In this paper,all bate values are assumed to be zero.In fact,the estimated value depends on the sampling period,so a better method may be adopted to obtain a more accurate bate estimation.(4)In this paper,we assume that the price of cash commodity obeys the geometric Brownian motion just like the price of futures.Of course,we can use some other models,such as the mean reversion process,to simulate the price of cash commodity.