【摘要】：在金融市場(chǎng)中，GARCH模型對(duì)金融時(shí)間序列波動(dòng)性的解釋，已經(jīng)得到大多數(shù)學(xué)者的認(rèn)同。但是對(duì)于不同的市場(chǎng)之間，或者不同的資產(chǎn)之間，往往存在著相互影響波動(dòng)的相關(guān)關(guān)系。同時(shí)為了分散、化解金融風(fēng)險(xiǎn)，，需要對(duì)多個(gè)資產(chǎn)進(jìn)行組合，進(jìn)行風(fēng)險(xiǎn)的對(duì)沖和規(guī)避，這些都是建立在對(duì)多個(gè)市場(chǎng)之間相關(guān)特性進(jìn)行分析的基礎(chǔ)之上。由于常用與研究多變量問題的多元GARCH模型在參數(shù)估計(jì)、多元分布假設(shè)等問題上存在一定的局限性，而Copula技術(shù)能解決這一些問題。Copula函數(shù)不僅為我們提供了一條在不考慮邊緣分布的情況下分析多元分布相關(guān)結(jié)構(gòu)的途徑，還為求取聯(lián)合分布函數(shù)提供了一條便捷的通道。而且若對(duì)變量作單調(diào)增的變換，相應(yīng)的Copula函數(shù)不變，因而有Copula函數(shù)導(dǎo)出的一致性質(zhì)和相關(guān)性測(cè)度的值也不會(huì)變化。本文結(jié)合Copula技術(shù)與GARCH模型來處理金融風(fēng)險(xiǎn)分析中多個(gè)資產(chǎn)間或多個(gè)市場(chǎng)間的相關(guān)性就方便多了。另外，在大多數(shù)文獻(xiàn)中，對(duì)于GARCH模型、Copula函數(shù)的參數(shù)估計(jì)是采用頻率學(xué)派的觀點(diǎn)，而利用參數(shù)的先驗(yàn)信息采用貝葉斯參數(shù)估計(jì)，能更充分挖掘出數(shù)據(jù)中的信息，另一方面也擴(kuò)充了模型估計(jì)的方法。最后本文的實(shí)證部分，針對(duì)上證綜合指數(shù)和深證成指，利用T-Copula-GARCH-T模型預(yù)測(cè)0.05置信度下的1天提前期VaR值。
[Abstract]:The interpretation of financial time series volatility by GARCH model in financial markets has been accepted by most scholars. However, for different markets, or between different assets, there is often a correlation between the impact of volatility. At the same time, in order to disperse and resolve financial risks, it is necessary to combine multiple assets, hedge and circumvent the risks, which are based on the analysis of the related characteristics of multiple markets. The multivariate GARCH model, which is commonly used and studied for multivariate problems, has some limitations in parameter estimation, multivariate distribution assumptions, and so on. Copula technology can solve these problems. Copula function not only provides a way to analyze the correlation structure of multivariate distribution without considering the edge distribution, but also provides a convenient way to obtain the joint distribution function. Moreover, if the variable is monotonously increased, the corresponding Copula function is invariant, so the uniform property derived by the Copula function and the value of the correlation measure will not change. This paper combines Copula technology and GARCH model to deal with the correlation between multiple assets or markets in financial risk analysis. In addition, in most literatures, the parameter estimation of the Copula function of GARCH model is based on the viewpoint of frequency school, and Bayesian parameter estimation can be used to extract the information from the data more fully by using the prior information of the parameter. On the other hand, the method of model estimation is extended. Finally, in the empirical part of this paper, the T-Copula-GARCH-T model is used to predict the VaR value of 1-day lead time under the confidence level of 0.05 for Shanghai Composite Index and Shenzhen Stock Exchange Index.
【學(xué)位授予單位】：廣州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F830.91;F224;O212.1

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