本文選題：火電企業(yè)價(jià)格風(fēng)險(xiǎn) + Copula-MSM-GARCH模型��； 參考：《中國(guó)礦業(yè)大學(xué)(北京)》2017年博士論文

【摘要】：當(dāng)前我國(guó)正處在電力市場(chǎng)化改革攻堅(jiān)期和二氧化碳排放配額交易計(jì)劃正式啟動(dòng)期�；痣娖髽I(yè)價(jià)格風(fēng)險(xiǎn)源從單一的煤炭?jī)r(jià)格波動(dòng)轉(zhuǎn)變?yōu)槊禾俊㈦娏投趸寂欧排漕~三種價(jià)格波動(dòng)。三者波動(dòng)規(guī)律都有著各自的特點(diǎn),且互相影響、互相依賴。在此背景下,未來火電企業(yè)價(jià)格風(fēng)險(xiǎn)管理問題必將變得更加復(fù)雜。因此,煤炭、電力和二氧化碳排放配額三者各自的波動(dòng)規(guī)律、彼此的相關(guān)性結(jié)構(gòu)、集成風(fēng)險(xiǎn)預(yù)測(cè)、風(fēng)險(xiǎn)對(duì)沖等問題都亟待研究。本文為煤炭、電力和二氧化碳排放配額各自的波動(dòng)規(guī)律建立了多種模型,包括馬爾科夫轉(zhuǎn)換多重分形(MSM)模型和經(jīng)典GARCH模型,用MSE、MAE、SPA等波動(dòng)率預(yù)測(cè)評(píng)價(jià)方法比較了各種模型設(shè)計(jì)規(guī)格的波動(dòng)率預(yù)測(cè)能力。在眾多的Copula函數(shù)中選擇了學(xué)生t-Copula函數(shù)作為連接函數(shù),新建了一個(gè)Copula-MSM-GARCH模型,并評(píng)價(jià)了該模型的波動(dòng)率預(yù)測(cè)能力和最小CVaR投資組合構(gòu)建能力。此外,本文還提出了以期貨交易把現(xiàn)實(shí)生產(chǎn)中的商品組合調(diào)整為條件在險(xiǎn)值(CVaR)最小投資組合的風(fēng)險(xiǎn)對(duì)沖策略。本文各章安排如下:前言部分首先限定了本文研究對(duì)象為火電企業(yè)價(jià)格風(fēng)險(xiǎn),隨后在研究背景與意義中介紹了中國(guó)正面臨推進(jìn)電力市場(chǎng)化改革和開啟二氧化碳排放配額交易的時(shí)代背景,并說明了當(dāng)前電力市場(chǎng)化改革和二氧化碳排放配額交易啟動(dòng)使火電企業(yè)面對(duì)的風(fēng)險(xiǎn)因素發(fā)生變化,急需建立更好的集成風(fēng)險(xiǎn)預(yù)測(cè)模型。在國(guó)內(nèi)外研究現(xiàn)狀中,介紹了國(guó)內(nèi)外關(guān)于電力、二氧化碳排放配額和煤炭三種商品價(jià)格波動(dòng)規(guī)律建模的相關(guān)研究,還介紹了國(guó)內(nèi)外投資組合集成風(fēng)險(xiǎn)預(yù)測(cè)的相關(guān)研究。此外,引言部分還詳細(xì)闡述了本研究的研究?jī)?nèi)容和所用方法,列出了本文的研究思路,以為撰寫全文做準(zhǔn)備。第二章研究電力價(jià)格波動(dòng)規(guī)律。之前學(xué)者研究發(fā)現(xiàn),電價(jià)具有多重分形性。本文通過對(duì)電價(jià)的多重分型模形的研究接受了該發(fā)現(xiàn)。本章針對(duì)電力價(jià)格的波動(dòng)規(guī)律,建立了一個(gè)馬爾科夫轉(zhuǎn)換多重分形(MSM)模型。選取EEX數(shù)據(jù)進(jìn)行實(shí)證檢驗(yàn)。有關(guān)電力現(xiàn)貨價(jià)格波動(dòng)率預(yù)測(cè)表現(xiàn)的研究顯示,多重分形模型有能力比GARCH模型在該方面做得更好。第三章研究二氧化碳排放配額價(jià)格波動(dòng)規(guī)律。本章研究了EU ETS的二氧化碳排放配額的短期現(xiàn)貨價(jià)格波動(dòng)規(guī)律。在回顧了該類新興資產(chǎn)的典型事實(shí)后本章研究了多種方法以建模排放配額波動(dòng)規(guī)律�？v觀不同階段價(jià)格和回報(bào)率的行為,本章建議用馬爾科夫轉(zhuǎn)換模型和AR-GARCH模型進(jìn)行隨機(jī)建模。為了檢驗(yàn)這些方法,本章進(jìn)行了樣本內(nèi)和樣本外預(yù)測(cè)分析,并比較了兩種方法的預(yù)測(cè)結(jié)果準(zhǔn)確度。結(jié)果是,該模型足以捕捉到如偏性,超額峰度和不同階段波動(dòng)率行為在內(nèi)的主要特征。第四章研究煤炭?jī)r(jià)格波動(dòng)規(guī)律。本章采用馬爾科夫轉(zhuǎn)換多重分形模型和一連串廣義自回歸條件異方差(GARCH)類模型來建模并預(yù)測(cè)煤炭?jī)r(jià)格波動(dòng)率。本章延伸了魏宇等(2010)和王玉東等(2016)之前的研究[57-58],用預(yù)測(cè)能力優(yōu)越性檢驗(yàn)(SPA)評(píng)價(jià)了所有這些模型的預(yù)測(cè)表現(xiàn)。為了準(zhǔn)確預(yù)測(cè)煤炭?jī)r(jià)格波動(dòng)率,本章嘗試應(yīng)用了多種不同類型的MSM模型。本文還在考慮了波動(dòng)率與VaR兩種風(fēng)險(xiǎn)度量方法。通過比較其他研究所用模型和MSM模型的預(yù)測(cè)表現(xiàn),本章確定,新的MSM模型在各種預(yù)測(cè)時(shí)域上都優(yōu)于其他模型。該優(yōu)越性也適用于VaR的預(yù)測(cè)。第五章研究火電企業(yè)集合價(jià)格風(fēng)險(xiǎn)對(duì)沖策略。利用條件Copula函數(shù)體系,本章可以分別建模相關(guān)性結(jié)構(gòu)與各商品價(jià)格波動(dòng)率。之前各章已經(jīng)篩選出了煤炭、電力和二氧化碳排放配額價(jià)格的波動(dòng)規(guī)律最合適的模型設(shè)計(jì)規(guī)格。在此基礎(chǔ)上,本章找到了最適合連接煤炭、電力MSM模型與二氧化碳排放配額GARCH模型的學(xué)生t-Copula函數(shù),從而建立了Copula-MSM-GARCH模型,并用歐洲能源交易所EEX數(shù)據(jù)做了實(shí)證。本章也展示了Copula-MSM-GARCH模型配合Monte Carlo模擬法如何被用于發(fā)現(xiàn)最小CVaR商品組合,并提出最小CVaR商品組合可以作為風(fēng)險(xiǎn)對(duì)沖策略的標(biāo)桿,以期貨交易把現(xiàn)實(shí)生產(chǎn)中的價(jià)格風(fēng)險(xiǎn)組合調(diào)整為最小CVaR投資組合可以使預(yù)期價(jià)格風(fēng)險(xiǎn)最小化。第六章為結(jié)論與展望。本章總結(jié)了全文的研究成果,提出了論文研究的創(chuàng)新點(diǎn)和研究存在的不足之處,并對(duì)下一步工作進(jìn)行展望。
[Abstract]:At present, China is in the hard period of the reform of the electricity market and the official start of the carbon dioxide emission quota trading plan. The price risk sources of the thermal power enterprises change from the single coal price fluctuation to three kinds of price fluctuations of coal, electricity and carbon dioxide emissions. The three fluctuation laws have their own characteristics, and they interact with each other and depend on each other. In this context, the price risk management of thermal power plants will become more complicated in the future. Therefore, the fluctuation laws of coal, electricity and carbon dioxide emissions quotas, the correlation structure of each other, the integrated risk prediction, and the risk hedging are urgently needed to be studied. This article is for the coal, electricity and carbon dioxide emission quotas each of the three. A variety of models are established, including the Markoff transform multifractal (MSM) model and the classical GARCH model. The volatility prediction ability of various model design specifications is compared with the volatility prediction evaluation methods such as MSE, MAE and SPA. The students' t-Copula function is selected as the connection function in many Copula functions, and a new Co is built. Pula-MSM-GARCH model, and evaluation of the volatility prediction ability of the model and the minimum CVaR portfolio construction capability. In addition, this paper also puts forward the risk hedging strategy of adjusting the commodity portfolio in real production to the risk value (CVaR) minimum portfolio. The chapters are arranged as follows: the preface is first limited The object of this paper is the price risk of thermal power enterprises. Then, the background and significance of the research are introduced in the background and significance of China. China is facing the background of promoting the reform of the electricity market and opening the carbon dioxide emission quota transaction, and explains the risk factors of the current electricity market reform and the start of the carbon dioxide emission quota transaction. There is an urgent need to establish a better integrated risk prediction model. In the domestic and foreign research status, this paper introduces the relevant research on the modeling of three commodity price fluctuation laws of electricity, carbon dioxide emission quota and coal, and also introduces the related research on the integrated risk prediction of domestic and foreign investment portfolio integration. In addition, the introduction is also detailed. The research contents and methods used in this study are expounded, the research ideas of this paper are listed, and the full text is prepared. The second chapter studies the law of electricity price fluctuation. The previous scholars found that the electricity price is multifractal. A Markov switching multifractal (MSM) model is established. EEX data is selected for empirical test. Research on the prediction performance of electric spot price volatility shows that the multifractal model has the ability to do better than the GARCH model. The third chapter studies the price fluctuation law of carbon dioxide emission quota. After reviewing the typical facts of the EU ETS carbon dioxide emissions quotas, this chapter studies a variety of methods to model the fluctuation of emission quotas in this chapter. A survey of the behavior of different stages of price and rate of return is proposed in this chapter. The Marco transform model and the AR-GARCH model are proposed for random construction in this chapter. In order to test these methods, this chapter carries out the prediction analysis within and outside the sample, and compares the accuracy of the prediction results of the two methods. The result is that the model is sufficient to capture the main characteristics such as deviation, excess kurtosis and different stages of wave rate behavior. The fourth chapter studies the fluctuation law of coal price. This chapter adopts Markoff Transform multifractal model and a series of generalized autoregressive conditional heteroscedasticity (GARCH) model to model and predict coal price volatility. This chapter extends the study [57-58] before Wei Yu et al (2010) and Wang Yudong (2016). The prediction performance of all these models is evaluated with predictive superiority test (SPA). In order to predict coal accurately The price volatility, this chapter attempts to apply a variety of different types of MSM models. This paper also considers the volatility and VaR two kinds of risk measurement methods. By comparing the models used in other research and the prediction of the MSM model, this chapter determines that the new MSM model is superior to the other models in the various prediction time domain. This superiority is also applicable to VaR. The fifth chapter studies the hedging strategy of aggregate price risk in thermal power enterprises. Using the conditional Copula function system, this chapter can model the correlation structure and the price volatility of each commodity respectively. The previous chapters have screened the most suitable model design specifications for the fluctuation rules of coal, electricity and carbon dioxide emissions quotas. In this chapter, we find a student t-Copula function which is most suitable for connecting coal, electric power MSM model and carbon dioxide emission quota GARCH model, thus establishing the Copula-MSM-GARCH model and using the EEX data of the European energy exchange. This chapter also shows how the Copula-MSM-GARCH model is used to find the smallest C with the Monte Carlo simulation method. VaR commodity combination, and proposed that the minimum CVaR portfolio can be used as a benchmark for risk hedging strategy. The adjustment of price risk combination in real production to minimum CVaR portfolio can minimize expected price risk by futures trading. The sixth chapter is the conclusion and prospect. This chapter summarizes the research results of the full text, and puts forward the research paper. Innovation and research deficiencies, and prospects for the next step.

【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)(北京)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224;F426.61

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