本文關(guān)鍵詞： Copula VaR 時(shí)變Copula 蒙特卡洛模擬　出處：《西南交通大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)前的金融市場(chǎng),隨著金融改革的深入化,金融市場(chǎng)的聯(lián)系日益緊密,投資組合、金融風(fēng)險(xiǎn)管理更是成為學(xué)者們關(guān)注的熱點(diǎn)問題。投資組合風(fēng)險(xiǎn)的研究,首先就是要研究金融變量之間的相關(guān)性關(guān)系。對(duì)于研究投資組合的相關(guān)關(guān)系,傳統(tǒng)投資組合一般都是以正態(tài)分布為假設(shè)前提的,但實(shí)際金融市場(chǎng)的數(shù)據(jù)并不都是服從正態(tài)分布。同時(shí),相關(guān)性分析的常用方法就是線性相關(guān)系數(shù)的分析,線性相關(guān)系數(shù)要求變量之間的關(guān)系是線性的,而金融資產(chǎn)組合之間的關(guān)系并不都是線性相關(guān)關(guān)系,也有很多資產(chǎn)組合之間存在非線性關(guān)系,此時(shí)也就無法用線性相關(guān)系數(shù)來準(zhǔn)確地描述金融資產(chǎn)之間的相關(guān)性。所以,在正態(tài)分布假設(shè)下,僅使用線性相關(guān)來分析資產(chǎn)組合之間的相關(guān)性,進(jìn)而計(jì)算其投資組合的風(fēng)險(xiǎn)價(jià)值往往與實(shí)際的風(fēng)險(xiǎn)價(jià)值是存在差異的。因此本文引入Copula函數(shù),作為分析變量之間相關(guān)性的工具,無論是變量之間的線性關(guān)系還是非線性關(guān)系,Copula函數(shù)都能夠很好地處理并且較好地描述。此外,目前采用Copula函數(shù)研究的相關(guān)關(guān)系大都假定是不變的,但在實(shí)際的金融市場(chǎng)中,國(guó)家政策變動(dòng)、金融事件的發(fā)生,都會(huì)引起經(jīng)濟(jì)市場(chǎng)中各種變量之間各種關(guān)系的變動(dòng),其中變量之間的相關(guān)性就不會(huì)靜止不變,而是會(huì)會(huì)隨著時(shí)間變化而變化,所以,本文在利用Copula函數(shù)的基礎(chǔ)上又進(jìn)一步利用時(shí)變Copula函數(shù)來研究資產(chǎn)組合的相關(guān)關(guān)系。 本文首先是介紹本文的研究背景以及意義,通過回顧國(guó)內(nèi)外研究現(xiàn)狀,指出傳統(tǒng)關(guān)于相關(guān)性研究方法的不足之處,并指出Copula理論在分析變量之間相關(guān)關(guān)系的必要性,同時(shí)對(duì)已有研究文獻(xiàn)進(jìn)行了評(píng)述；然后詳盡地介紹Copula函數(shù)理論,包括Copula函數(shù)的性質(zhì)、定理、類型,然后介紹了Copula模型的構(gòu)建方法以及參數(shù)估計(jì)方法、模型評(píng)價(jià)和檢驗(yàn)方法,最后著重介紹時(shí)變t-Copula模型；同時(shí)對(duì)于VaR也作了全面的闡述,其中介紹了其產(chǎn)生、影響因素以及VaR的三種計(jì)算方法,并且舉例說明了VaR的計(jì)算方法。在介紹了Copula和VaR理論的基礎(chǔ)上,將時(shí)變Copula函數(shù)理論應(yīng)用到風(fēng)險(xiǎn)管理中的實(shí)證分析,通過選擇滬深股票市場(chǎng)下的投資組合,先用歷史數(shù)據(jù)確定邊緣分布,然后選擇幾種不同的Copula函數(shù)和時(shí)變Copula來描述資產(chǎn)之間的相關(guān)性,進(jìn)而度量出相應(yīng)投資組合風(fēng)險(xiǎn),并進(jìn)行比較和評(píng)價(jià),發(fā)現(xiàn)在時(shí)變Copula能夠更好地估算資產(chǎn)組合的VaR。
[Abstract]:The current financial market, with the deepening of financial reform, the financial market is increasingly closely linked, portfolio, financial risk management has become a hot issue of concern to scholars. The first is to study the correlation between financial variables. For the study of portfolio correlation, the traditional portfolio is generally based on the assumption of normal distribution. At the same time, the commonly used method of correlation analysis is the analysis of linear correlation coefficient, the linear correlation coefficient requires that the relationship between variables is linear. But the relationship between the financial asset portfolio is not all linear correlation, and there are many nonlinear relationships between the portfolio, so the linear correlation coefficient can not be used to accurately describe the correlation between the financial assets. Under the assumption of normal distribution, only linear correlation is used to analyze the correlation between asset portfolios, and then the risk value of the portfolio is often different from the actual risk value. Therefore, the Copula function is introduced in this paper. As a tool for analyzing the correlation between variables, the Copula function, whether linear or nonlinear, can be well handled and well described. At present, most of the related relations studied by Copula function are assumed to be invariant, but in the actual financial market, the change of national policy and the occurrence of financial events will cause the changes of the relations between various variables in the economic market. The correlation between variables is not static, but will change with time. Therefore, based on the Copula function, we further use the time-varying Copula function to study the correlation of asset combinations. This paper first introduces the research background and significance of this paper, points out the shortcomings of the traditional methods of correlation research, and points out the necessity of Copula theory in analyzing the correlation relationship between variables by reviewing the current research situation at home and abroad. Then the theory of Copula function is introduced in detail, including the properties, theorems, types of Copula function, and then the construction method of Copula model, the method of parameter estimation, the method of model evaluation and test are introduced. Finally, the time-varying t-Copula model is introduced, and the VaR is also described, including its generation, influencing factors and three calculation methods of VaR. Based on the introduction of Copula and VaR theory, the time-varying Copula function theory is applied to the empirical analysis of risk management. Firstly, the edge distribution is determined by historical data, then several different Copula functions and time-varying Copula are selected to describe the correlation between assets, and then the corresponding portfolio risk is measured and compared and evaluated. It is found that in time-varying Copula, it can better estimate the value of portfolio.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F224;F832.51

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本文編號(hào)：1551946