Price Risk Forcast Model And Hedging Strategy Research Of Price Risk Of Thermal Power Enterprises

At present,China is facing the power market reform and carbon dioxide emition allowrance trading program's launching.Thermal power enterprise's price risk source switches from single coal price fluctuations into three kinds of price fluctuations,coal,electricity and carbon dioxide emition allowrance.The three kinds of commodities' fluctuations have their seprate characteristics,and influence each other mutually,dependent mutually.In this context,the future thermal power enterprise price risk management issues will become more complex.Therefore,coal,electricity and carbon dioxide emission allowrance's correlation structure,integration risk forecasting,risk minimization and other issues are urgent to be studied.In this paper,a variety of models are established for the dynamics of coal,electricity and carbon dioxide emission rights,including the Markov-switching multifractal(MSM)model and the classical GARCH model.This paper compares volatility prediction capability of various model specifications by MSE,MAE and SPA.A Copula-MSM-GARCH model was established by using the student t-Copula function as a connection function,and the volatility prediction ability and the minimum CVaR portfolio construction ability of the model were evaluated.In addition,this paper also puts forward adjusting the price risk portfolio in real electricity production to the minimum CVaR portfolio by futures trading,so that minimizing price risk.The chapters of this article are arranged as follows:The foreword part firstly defines the price risk of this research object,and then introduces the background of the reform of the electricity market and the starting of the carbon dioxide emission allowrance trade in China,and introduces the significance of the research,then explains the background's influences to the thermal power enterprises in the risk factors change.The above all lead the urgent need to establish a better integrated risk forecasting model.Secondly,this chapter introduces the domestic and foreign researches on the dynamics of electricity,carbon dioxide emission allowrance and coal price,and the domestic and foreign researches on the integration risk forecast.In addition,this chapter also elaborated the research content and the method used,listed the research path of this article.Chapter 2 studies the electricity price dynamic.Previous scholars have found that electricity prices have multifractal.The finding is accepted by this paper.A Markov-switching multifractal(MSM)model is established for the dynamic of electricity price.This chapter selects EEX data for empirical research.Researches on the performance of the electricity price volatility forecast show that the multifractal model has better ability in the area than the GARCH model.Chapter 3 studies the dynamic of EUA price.After reviewing the typical facts of EUA,this chapter examines a number of ways to model the EUA dynamic.Based on the volatility behavior of its price and rate of return in different stages,this chapter proposes a stochastic modeling with Markov-Switching model and AR-GARCH model.In order to compare these methods,this chapter compares the prediction results of the two methods by intra-sample and out-of-sample predictions.This chapter finds that the model is sufficient to capture the main features of time series,such as biased,excess kurtosis and heteroscedasticity.The chapter 4 studies the coal price dynamic.This chapter uses MSM models and a series of GARCH model to predict coal price volatility.This paper extends the previous studies by Wei et al.(2010)and Wang et al.(2016),and evaluated the predictive performance of all these models using the SPA.In order to accurately predict the volatility of coal prices,this paper attempts to apply a variety of different types of MSM models.This paper also considers two methods of risk measurement of volatility and VaR.By comparing the predictive performance of the other models and the MSM model,it is concluded that the new MSM model is superior to other models in various prediction time domains.The superiority is also applied to the prediction of VaR.Chapter 5 studies the price risk hedge strategy of thermal power enterprises.Conditional Copula function framework allows this chapter to model the dependency and the commodity price volatility respectively.Based on the study of the dynamics of coal,electricity and EUA in previous chapters,this chapter finds the most suitable model specifications for modeling the dynamics of various commodity prices.In order to study the integrated risk of the three,this chapter proposes the Copula-MSM-GARCH model and uses the EEX data to do the empirical study.This chapter also shows that the Copula-MSM-GARCH model can be used to find the minimized CVaR portfolio and propose that adjust the price risk portfolio in real production to the smallest CVaR portfolio by futures trading.Chapter 6 is the conclusion and prospect.This chapter summarizes the research results of the whole paper,puts forward the innovation of the paper and the shortcomings of the research,and prospects for the next step.