本文關(guān)鍵詞：基于非參數(shù)GARCH-EVT模型的上證市場風(fēng)險度量　出處：《南京財經(jīng)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 市場風(fēng)險 動態(tài)VaR 非參數(shù) GARCH模型 極值理論

【摘要】：隨著金融市場在我國不斷的發(fā)展,作為其重要組成部分的股票市場的風(fēng)險也愈發(fā)的受到關(guān)注和重視。本文擬研究上海證券市場的市場風(fēng)險,對度量市場風(fēng)險的傳統(tǒng)方法進行了改進和補充,力圖得到一種可以廣泛使用且更為準(zhǔn)確的測度模型。對于風(fēng)險的測度,當(dāng)今的主要方法是估算風(fēng)險價值(VaR),這是應(yīng)用最為廣泛的一種度量風(fēng)險的工具。該方法將原本抽象、不易描述的風(fēng)險變成了直觀的數(shù)字,具有簡潔明了的特點。但傳統(tǒng)VaR計算方法都需要較多的前提假定,很多情況下這些假設(shè)與現(xiàn)實是不相符的,這就降低了模型的可信性。本文將非參數(shù)GARCH模型和極值模型(EVT)相結(jié)合,充分利用這兩個理論的特點,達到優(yōu)化VaR估計方法的目的。利用非參數(shù)GARCH模型可以較好的克服原始收益率數(shù)據(jù)的群聚波動性,并且獲得優(yōu)于其他GARCH族模型的擬合結(jié)果,從而為極值模型提供理想的原始數(shù)據(jù)；極值理論只研究市場數(shù)據(jù)分布的尾部特征,進而避免了對整個數(shù)據(jù)進行分布假設(shè)；非參數(shù)GARCH族模型和極值模型的結(jié)合,估計出的動態(tài)VaR值也優(yōu)于傳統(tǒng)方法的靜態(tài)VaR。本文首先介紹了當(dāng)前金融市場的發(fā)展情況和金融市場風(fēng)險的重要意義,總結(jié)了風(fēng)險度量理論在國內(nèi)外的研究現(xiàn)狀,介紹了VaR的基本概念、計算原理以及方法。其次本文建立了非參數(shù)GARCH模型,通過理論和實證檢驗證明了其優(yōu)于其他GARCH族模型,特別在計算的VaR時,其優(yōu)點可以得到更大的發(fā)揮；最后,在極值理論的基礎(chǔ)上,得到VaR結(jié)果,并將極值模型計算的靜態(tài)VaR值和非參數(shù)GARCH-EVT模型計算的動態(tài)VaR值的準(zhǔn)確率進行了對比,通過比較發(fā)現(xiàn),利用非參數(shù)GARCH-EVT模型明顯的提高了VaR的計算準(zhǔn)確率。本文的創(chuàng)新之處主要是將非參數(shù)GARCH模型引入到股票市場的風(fēng)險度量中,證明了其在估計VaR時優(yōu)于其他GARCH族模型。同時,針對VaR的估計,本文引入了極值理論,并通過建立一系列的方法確定較為客觀的閾值；最后將非參數(shù)GARCH模型與極值模型相結(jié)合,組成非參數(shù)GARCH-EVT模型,擴大了傳統(tǒng)方法的應(yīng)用范圍,放松了傳統(tǒng)假定,得到了更為準(zhǔn)確穩(wěn)定的估計結(jié)果。
[Abstract]:With the development of financial market in the continuous development of our country, as an important part of the risk of the stock market has become more and more concern and attention. This paper intends to study the Shanghai stock market risk, the traditional method for measuring market risk is improved and added, force diagram which can be widely used and more measure model accurate. For the risk measure, the main method is the estimation of value at risk (VaR), which is a risk measurement tool widely used. The method of the abstract, the risk is not easy to describe a visual digital, has the characteristics of simple. But the traditional VaR calculation method is the premise we need more assumptions, in many cases, these assumptions are not consistent with the reality, which can reduce the reliability of the model. In this paper, the parameters of GARCH model and extreme value model (EVT) combined with full use of the two The characteristics of the theory, to optimize the VaR estimation method. Using the nonparametric GARCH model can overcome the original rate of return data clustering volatility, and obtain the fitting results is better than the other GARCH models, the original data so as to provide an ideal model of extreme value; extreme value theory only on the tail characteristics of market data distribution. To avoid the data distribution hypothesis; combined with non parametric GARCH models and extreme value model, dynamic VaR estimated value is superior to the traditional method of static VaR., this paper firstly introduces the important significance of the development and the current financial market risk of financial market, summarizes the current situation of research on risk measurement theory at home and abroad. The basic concept of VaR is introduced, the calculation principle and method. Secondly, this paper established the parameters of GARCH model, through theoretical and empirical test proves that it is superior to the He GARCH model, especially in the calculation of VaR, its advantages can be a greater role; finally, based on the extreme value theory, get the results of VaR, and the accuracy of dynamic VaR calculation model for the calculation of the static VaR extreme value and non parametric GARCH-EVT model value were compared, by comparison obviously, improve the calculation accuracy of VaR by non parametric GARCH-EVT model. The main innovation of this paper is the non parametric GARCH model is introduced to the stock market risk measurement, prove the estimation of VaR is superior to the other GARCH models. At the same time, according to VaR estimates, this paper introduces the extreme value theory, and more objective to determine the threshold value by setting up a series of methods; finally, combined with the non parametric GARCH model and extreme value model, non parametric GARCH-EVT model, expanding the scope of application of the traditional method, the traditional assumption was relaxed, The results are more accurate and stable.

【學(xué)位授予單位】：南京財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.51;F224

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本文編號：1360120