本文關(guān)鍵詞：半?yún)?shù)C-Vine Copula模型理論及其金融風(fēng)險結(jié)構(gòu)測度研究　出處：《天津財經(jīng)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

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【摘要】：2016年,作為第十三個五年規(guī)劃的開局之年,我國經(jīng)濟(jì)已經(jīng)進(jìn)入"新常態(tài)"。"新常態(tài)"不是一個固定不變的狀態(tài),而是一個動態(tài)優(yōu)化的過程。在此過程中,經(jīng)濟(jì)和金融市場的各個指標(biāo)都呈現(xiàn)出了新的特點(diǎn),其內(nèi)在風(fēng)險關(guān)聯(lián)更加復(fù)雜。這使得傳統(tǒng)的經(jīng)濟(jì)計量模型難以有效地測度風(fēng)險相關(guān)結(jié)構(gòu),需要研究新的理論工具解決"新常態(tài)"下的風(fēng)險結(jié)構(gòu)測度問題。由于半?yún)?shù)C-VineCopula具有穩(wěn)健靈活易于估計的特性,可以很好地解決此方面的問題,所以對該函數(shù)的研究具有重要的理論意義與應(yīng)用價值。Copula函數(shù)模型是一種新型多元聯(lián)合分布建模工具。其顯著特點(diǎn)是兩步法構(gòu)造,使得各個變量的個體特征體現(xiàn)在其邊緣分布中,而相關(guān)性結(jié)構(gòu)體現(xiàn)在Copula函數(shù)中。與傳統(tǒng)的多元正態(tài)、多元t分布比,該模型放松了假設(shè)條件,構(gòu)造更為靈活簡便,應(yīng)用更為廣泛,能夠更為真實地擬合經(jīng)濟(jì)變量的聯(lián)合分布。鑒于以上優(yōu)點(diǎn),根據(jù)目前金融風(fēng)險結(jié)構(gòu)的特征,論文對Copula函數(shù)模型進(jìn)行了以下幾個方面的研究:論文首先對Copula函數(shù)的中外文獻(xiàn)進(jìn)行了梳理和總結(jié)。其次,對相關(guān)性測度指標(biāo)及Copula函數(shù)建模的邊緣分布、函數(shù)模型、估計和檢驗等相關(guān)步驟中所用工具的理論性質(zhì)和應(yīng)用范圍進(jìn)行了詳細(xì)而深入的研究。再次,就非參數(shù)核密度估計方法與較為流行的Vine Copula模型的結(jié)合進(jìn)行了研究,構(gòu)造了半?yún)?shù)C-Vine Copula模型。最后,在以上研究的基礎(chǔ)上,針對美、中、日、德、英五國股票市場的風(fēng)險相關(guān)性程度及結(jié)構(gòu)進(jìn)行了測度研究。并就其經(jīng)濟(jì)意義進(jìn)行了討論和分析。論文的主要創(chuàng)新體現(xiàn)在以下幾個方面:第一,提出了在數(shù)據(jù)樣本數(shù)量較少或存在內(nèi)在非線性轉(zhuǎn)換機(jī)制的情況下,可以使用STAR模型擬合邊緣分布,構(gòu)造STAR-Copula模型。第二,在非參數(shù)核密度方法研究部分,對二階核、高階核和非對稱核進(jìn)行了理論研究及在各種分布環(huán)境下的模擬,表明了正態(tài)核和非對稱核具有良好的估計性質(zhì),雙角核、三角核和Epanechnikov核存在較小的邊界效應(yīng)。第三,結(jié)合非參數(shù)核密度方法對數(shù)據(jù)限制較少的優(yōu)點(diǎn)和Vine Copula模型構(gòu)造靈活的優(yōu)點(diǎn),首創(chuàng)了半?yún)?shù)C-Vine Copula模型,并就其在各種環(huán)境下的估計性質(zhì)進(jìn)行了 MonteCarlo模擬,表明了該模型具有良好的穩(wěn)健性,適用于數(shù)據(jù)分布不規(guī)律以及數(shù)據(jù)異常波動情況下的多元聯(lián)合分布建模。論文創(chuàng)新給"新常態(tài)"金融風(fēng)險相關(guān)結(jié)構(gòu)測度研究提供了有力的定量分析工具。
[Abstract]:In 2016, as the thirteenth year of the opening of the five year plan, China's economy has entered a "new normal". The "new normal" is not a fixed state, but a process of dynamic optimization. In this process, the various indicators of the economic and financial markets have shown new features, and their internal risk association is more complex. This makes the traditional econometric model difficult to effectively measure the risk related structure, and we need to study new theoretical tools to solve the risk structure measurement problem under the new normal. Because semi parametric C-VineCopula has the characteristics of robust, flexible and easy to estimate, it can solve this problem well. Therefore, the study of this function has important theoretical significance and application value. The Copula function model is a new multicomponent joint distribution modeling tool. Its remarkable feature is the two step structure, which makes the individual characteristics of each variable in its edge distribution, and the correlation structure is embodied in the Copula function. Compared with the traditional multivariate normal distribution and multivariate t distribution, the model relaxes the hypothetical condition, and it is more flexible, simple and widely applied, which can more accurately match the joint distribution of economic variables. In view of the above advantages, according to the characteristics of the current financial risk structure, the paper studies the following aspects of Copula function model: first, the paper summarizes and summarizes the Chinese and foreign literature of Copula function. Secondly, we have done detailed and in-depth research on the theoretical nature and application scope of the tools used in the related steps, such as the correlation measure index and the marginal distribution, function model, estimation and inspection, which are modeled by the Copula function. Thirdly, the combination of the non parametric kernel density estimation method and the more popular Vine Copula model is studied, and a semi parametric C-Vine Copula model is constructed. Finally, on the basis of the above research, we measure the degree and structure of risk correlation in the stock market of the five countries of the United States, China, Japan, Germany and Britain. And its economic significance is discussed and analyzed. The main innovations of the paper are as follows: first, it is proposed that when the number of data samples is small or there is an inherent nonlinear transformation mechanism, we can use the STAR model to fit the edge distribution and construct the STAR-Copula model. The second part, in the study of non parametric kernel density method, the two order nuclear, nuclear and non nuclear high order symmetry was studied and the distribution in various simulation environment, show the normal nuclear and non symmetric kernel estimation with good properties of double angle triangular nucleus, nucleus and the Epanechnikov nucleus in the boundary effect small. Third, combined with the non parametric kernel density method for less data limitations and the advantages of Vine Copula model to construct the advantages of flexible, pioneered the semiparametric C-Vine model of Copula, and the MonteCarlo simulation of the environment in a variety of estimation properties, the results show that this model has good robustness and is suitable for the data distribution rule and multivariate data modeling the joint distribution under the condition of abnormal fluctuations. The paper provides a powerful quantitative analysis tool for the study of the structure measurement of the "new normal" financial risk related structure.
【學(xué)位授予單位】：天津財經(jīng)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：F224;F831.51

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