本文關(guān)鍵詞： 風(fēng)險(xiǎn)管理 VaR、CVaR、極值理論 Copula函數(shù)　出處：《華中師范大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著金融全球化進(jìn)程的加快,金融市場面臨的風(fēng)險(xiǎn)日益復(fù)雜化和多樣化,有效的進(jìn)行風(fēng)險(xiǎn)管理成為金融行業(yè)的重中之重。而風(fēng)險(xiǎn)管理的關(guān)鍵在于對風(fēng)險(xiǎn)價(jià)值的測量,如何精確的度量不同形式的風(fēng)險(xiǎn)成為學(xué)術(shù)界和金融界關(guān)注的熱點(diǎn)和難點(diǎn)。本文搜集了全球主要的六支股指,從不同的投資決策出發(fā),全而系統(tǒng)的運(yùn)用不同的方法針對不同形式的資產(chǎn)價(jià)值估算風(fēng)險(xiǎn)價(jià)值。 本文有兩條主線：一是資產(chǎn)形式,單一資產(chǎn)資產(chǎn)形式如滬深300,如果觀察時(shí)變風(fēng)險(xiǎn),本文采用基于GARCH類模型的參數(shù)估計(jì)法來估計(jì)其時(shí)變風(fēng)險(xiǎn),如果投資者側(cè)重于關(guān)注極值風(fēng)險(xiǎn),本文運(yùn)用極值理論來求其極值風(fēng)險(xiǎn)；對于資產(chǎn)組合形式,投資者或者投資機(jī)構(gòu)不僅關(guān)注每項(xiàng)資產(chǎn)間的相關(guān)關(guān)系,還重視其相關(guān)模式,本文結(jié)合極值理論與Copula函數(shù)理論來求其在險(xiǎn)價(jià)值。二是估計(jì)方法,本文按照從參數(shù)估計(jì)到半?yún)?shù)估計(jì)到非參數(shù)估計(jì)的思路對針對不同的投資決策來估計(jì)風(fēng)險(xiǎn)價(jià)值�？此剖菍︼L(fēng)險(xiǎn)測度方法的討論,實(shí)則以研究方法為技術(shù)支撐來達(dá)到精確測度風(fēng)險(xiǎn)價(jià)值的目的。 大量的研究成果表明：金融時(shí)間序列收益率的分布呈現(xiàn)尖峰厚尾、波動(dòng)的集聚性等特征,而不是傳統(tǒng)假設(shè)的正態(tài)分布。針對這一特點(diǎn)本文的第三三章以討論了滬深300的收益特征并在估算在險(xiǎn)價(jià)值。本文采用偏態(tài)t分布下的FIGARCH模型來估計(jì)時(shí)變VaR。檢驗(yàn)結(jié)果表明偏態(tài)t分布下的VaR估計(jì)優(yōu)越于傳統(tǒng)的正態(tài)分布、學(xué)生t分布、GED分布下的估計(jì)值。在金融風(fēng)險(xiǎn)的管理中,投資者往往更關(guān)注極值事件,所以如何合理的刻畫極值分布,求出極值情況下的風(fēng)險(xiǎn)價(jià)值尤為重要。本文第四章利用極值理分別描述所搜集的六支股指的尾部分布情況并估計(jì)出VaR、CvaR。根據(jù)風(fēng)險(xiǎn)分散化原理,大多數(shù)投資者都會(huì)進(jìn)行多元化的投資,以降低風(fēng)險(xiǎn)。這牽涉到多元極值的問題,第五章在極值理論的基礎(chǔ)之上引入Copula函數(shù)理論,來簡化多元極值問題。采用Copula-EVT模型分析由搜集到的六支股指等權(quán)重組成的資產(chǎn)組合的風(fēng)險(xiǎn)價(jià)值。失敗率檢驗(yàn)的結(jié)果表明：在95%和99%的顯著性水平下,失敗率和顯著性水平都很接近,說明多元t-Copula模型能較好的描述多資產(chǎn)的相依性結(jié)構(gòu),Copula-EVT的模型選擇適宜。 本文的最后對全文做了進(jìn)行了總結(jié),分析了本文研究的不足之處,在極值理論和Copula理論的研究現(xiàn)狀之上對未來的研究方向做了展望。
[Abstract]:With the acceleration of the process of financial globalization, the risks faced by financial markets are becoming increasingly complex and diversified. Effective risk management has become the most important part of the financial industry, and the key to risk management lies in the measurement of risk value. How to accurately measure different forms of risk has become a hot and difficult issue in academic and financial circles. This paper collects the six major stock indexes in the world and starts from different investment decisions. The whole system uses different methods to estimate the risk value for different forms of asset value. There are two main lines in this paper: one is asset form, one is single asset form, such as Shanghai and Shenzhen 300. If time-varying risk is observed, this paper uses parameter estimation method based on GARCH model to estimate time-varying risk, if investors focus on extreme value risk. In this paper, extreme value theory is used to calculate the extreme value risk. For the form of portfolio, investors or investment institutions not only pay attention to the relationship between each asset, but also attach importance to its related model. In this paper, the extreme value theory and Copula function theory are combined to find the value in danger. In this paper, according to the idea from parameter estimation to semi-parameter estimation to non-parametric estimation, we estimate the value of risk for different investment decisions. It seems to be a discussion of the method of risk measurement. In fact, the research method is taken as the technical support to achieve the purpose of accurately measuring the value of risk. A large number of research results show that the distribution of financial time series returns shows the characteristics of peak and thick tail, agglomeration of volatility, and so on. The 33th chapter of this paper discusses the income characteristics of CSI 300 and estimates its value at risk. In this paper, the FIGARCH model under skewed t distribution is used to estimate the time-varying VaR. The results show that the VaR estimation under skew t distribution is superior to the traditional normal distribution. Student t distribution is estimated under GED distribution. In financial risk management, investors tend to pay more attention to extreme value events, so how to describe the extreme value distribution reasonably, It is very important to find out the value of risk under extreme value. Chapter 4th describes the tail distribution of the six stock indexes collected and estimates the tail distribution of the six stock indexes collected in Chapter 4th. According to the principle of risk decentralization, Most investors make diversified investments to reduce risk. This involves the problem of multivariate extremum. Chapter 5th introduces Copula function theory on the basis of extreme value theory. To simplify the multivariate extremum problem. The Copula-EVT model is used to analyze the risk value of the portfolio composed of six stock indexes with equal weights. The results of the failure rate test show that: at the significance level of 95% and 99%, The failure rate and the significance level are very close, which indicates that the multivariate t-Copula model can better describe the dependence structure of multiple assets and the model selection of Copula-EVT is suitable. At the end of this paper, the author summarizes the full text, analyzes the deficiency of this paper, and looks forward to the future research direction on the basis of the research status of extreme value theory and Copula theory.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F830.91;F224

【引證文獻(xiàn)】

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本文編號：1550216