The Research On Weather Option Pricing Based Or Temperature Index

Weather changes have impact on the economic activities of many businesses.Faced with weather risk,more and more people choose to join in the risk management market for active management.The main hedging in the market is the disastrous weather risk and general weather risk.Among them,people have a deeper understanding of disastrous weather risk,and weather insurance to deal with disastrous risk has existed nearly one hundred years.Although the consequence of the general weather risk is light in the short term,they have the characteristics of long duration and wide range,which will have a negative impact on the company's operation.Therefore,practical methods are needed to avoid such risk.At the end of 20th century,weather derivatives filled this gap and developed rapidly.In this paper,we systematically introduce weather risk and weather risk management,comparatively analyze the differences and advantages of weather derivatives relative to weather insurance.Next,the types of weather derivatives are described,and with a specific case to analysis the effect of weather options in protecting the income of a company.Then,on the basis of existing research,we select daily average temperature of Zhengzhou from 1951 to 2020 as the sample,study the pricing of European weather options.Firstly,we analyze the statistical properties of temperature series,use Ornstein-Uhlenbeck(O-U)model based on daily volatility,O-U model based on monthly volatility and time series decomposition model to simulate the temperature series.When we establish the O-U model,the mean series is described by sinusoidal function,the daily volatility and monthly volatility are calculated by the difference between the original sequence and the mean sequence,the mean-reversion rate is estimated by martingale function;when we establish the time series decomposition model,according to the principle of addition decomposition the determinate term is extracted firstly,then the autoregressive moving average(ARMA)model is established to simulate the random term.Finally,the effect of the model is tested from the perspective of trend and index prediction error.The results show that:the temperature series presents the characteristics of periodicity and mean reversion,and the sequence of temperature differences is normal;the O-U model and time series decomposition model can simulate the temperature trend and fluctuation effectively;from the test error,the above models are suitable for the simulation of temperature series,and the O-U model based on monthly volatility is better.Based on temperature models,the weather options are priced by Monte Carlo simulation method.In this paper,we take the weather options based on temperature index of January and August as an example for pricing analysis,and calculate the pricing error when the baseline temperature is from 16? to 23?.The results show that:the pricing effect based on monthly volatility and Monte Carlo is better,and with the change of baseline temperature,the stability of the model is better;different baseline temperatures have a great influence on the model effect,then to ensure the accuracy of prediction,different baseline temperatures should be set for winter and summer.