本文關(guān)鍵詞：基于VaR模型的金融市場(chǎng)風(fēng)險(xiǎn)測(cè)量方法的探析　出處：《東北財(cái)經(jīng)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 風(fēng)險(xiǎn)價(jià)值(VaR) Copula-GARCH Monte Carlo

【摘要】：隨著科技和信息的不斷發(fā)展,全球經(jīng)濟(jì)和金融一體化的步伐不斷加快,金融市場(chǎng)的波動(dòng)性和風(fēng)險(xiǎn)也在不斷增加,對(duì)國(guó)家和企業(yè)的影響也越來(lái)越大,從而使金融風(fēng)險(xiǎn)管理已為越來(lái)越多的金融機(jī)構(gòu)和投資者所重視。風(fēng)險(xiǎn)的度量、分析技術(shù)發(fā)展得非常迅速。風(fēng)險(xiǎn)價(jià)值VaR (Value at Risk)方法起源于20世紀(jì)80年代,是一種衡量金融市場(chǎng)風(fēng)險(xiǎn)統(tǒng)計(jì)的方法,它被廣泛應(yīng)用于銀行,證券公司,大宗商品和其他貿(mào)易組織。這項(xiàng)研究方法的主要特點(diǎn)是度量和分析市場(chǎng)風(fēng)險(xiǎn)在金融市場(chǎng)中的風(fēng)險(xiǎn)價(jià)值,在現(xiàn)代金融風(fēng)險(xiǎn)管理中具有重要的地位。我國(guó)對(duì)VaR方法的應(yīng)用也在逐漸發(fā)展完善之中,對(duì)其進(jìn)行研究的內(nèi)容也有很多。其主要內(nèi)容是(1)預(yù)測(cè)未來(lái)可能出現(xiàn)的金融損失風(fēng)險(xiǎn)值計(jì)量。(2)識(shí)別危險(xiǎn)因素的分布如何影響投資組合的分布。(3)為投資組合優(yōu)化和風(fēng)險(xiǎn)管理提供可靠的信息。 傳統(tǒng)的VaR計(jì)量方法存在著一些缺點(diǎn),如正態(tài)分布的假設(shè),不滿足一致性公理,缺乏對(duì)極端事件和金融資產(chǎn)間的尾部相關(guān)性的考慮等。這些都會(huì)影響投資組合的投資風(fēng)險(xiǎn)和效果,影響VaR度量的精確度。而Copula的理論相對(duì)傳統(tǒng)方法有著理論優(yōu)勢(shì),Copula模型可以將相關(guān)關(guān)系和相關(guān)模式的研究有機(jī)地結(jié)合在一起。通過(guò)Copula函數(shù),可以捕捉到變量間非線性、非對(duì)稱和尾部的相關(guān)關(guān)系。Copula理論為分析多變量金融時(shí)間序列問(wèn)題提供了嶄新的思路。 本文主要的研究思路是考慮到金融市場(chǎng)中的波動(dòng)聚集現(xiàn)象,建立GARCH模型來(lái)測(cè)度金融市場(chǎng)風(fēng)險(xiǎn),并針對(duì)VaR方法的一些缺點(diǎn),引入Copula函數(shù),在邊際分布上針對(duì)金融數(shù)據(jù)中出現(xiàn)的尖峰厚尾現(xiàn)象分別假定變量服從正態(tài)分布,t分布,GED分布。通過(guò)失敗率檢驗(yàn)和K-S檢驗(yàn)確定了最優(yōu)的邊際分布模型和最優(yōu)Copula函數(shù)。將最優(yōu)邊際分布GARCH模型與最優(yōu)Copula函數(shù)相結(jié)合,通過(guò)Monte Carlo模擬法度量滬深指數(shù)的投資組合的VaR。 本文總共分成六部分,主要內(nèi)容如下： 第一章為引言,簡(jiǎn)要地概述課題研究的背景和意義,綜述國(guó)內(nèi)外有關(guān)VaR的研究現(xiàn)狀及國(guó)內(nèi)外利用Copula方法度量VaR的情況,最后對(duì)本文的研究?jī)?nèi)容、方法及創(chuàng)新與不足點(diǎn)做了簡(jiǎn)單說(shuō)明。 第二章是金融風(fēng)險(xiǎn)知識(shí)。簡(jiǎn)單概括了金融風(fēng)險(xiǎn)的定義和對(duì)金融風(fēng)險(xiǎn)度量的傳統(tǒng)方法。 第三章是VaR的理論基礎(chǔ)。首先,詳細(xì)介紹了VaR理論產(chǎn)生的現(xiàn)實(shí)背景,概念,VaR的持有期和置信水平的選擇。其次,重點(diǎn)闡述三種傳統(tǒng)的度量VaR的方法,主要包括德爾塔-正態(tài)法,歷史模擬法,Monte Carlo模擬法。然后,給出了計(jì)算VaR的模型和檢驗(yàn)方法。最后對(duì)VaR的優(yōu)缺點(diǎn)做了簡(jiǎn)單的介紹。 第四章為Copula函數(shù)相關(guān)知識(shí)。詳細(xì)解釋了Copula函數(shù)的定義及相關(guān)性質(zhì),介紹了Copula的常見函數(shù)和相關(guān)定理。簡(jiǎn)單敘述運(yùn)用Copula函數(shù)的相關(guān)性度量即Kendall's tau和Spearman's同時(shí),對(duì)尾部相關(guān)性做了簡(jiǎn)要說(shuō)明。最后詳細(xì)闡述了利用基于Copula-GARCH模型的Monte Carlo模擬法計(jì)算VaR的方法。 第五章為論文的實(shí)證分析部分。本文構(gòu)造的投資組合為上證指數(shù)和深證綜指按等權(quán)組合,時(shí)間為2008.1.1-2013.4.3,總共是1278組數(shù)據(jù)。 采取對(duì)數(shù)收益率進(jìn)行實(shí)證分析,首先估計(jì)邊際分布模型,通過(guò)分析數(shù)據(jù)特征,檢驗(yàn)波動(dòng)性,平穩(wěn)性,自相關(guān)-偏自相關(guān)以及ARCH效應(yīng),分別在正態(tài)分布,t分布,GED分布下進(jìn)行回歸,通過(guò)進(jìn)一步的計(jì)量分析,確定最優(yōu)的模型為GARCH(1,1)-t模型。 然后通過(guò)K-S檢驗(yàn)認(rèn)為t-Copula函數(shù)為描述變量間相關(guān)性最合適的Copula函數(shù),并利用秩相關(guān)系數(shù)進(jìn)行驗(yàn)證。本文將t-Copula函數(shù)結(jié)合GARCH-t模型來(lái)對(duì)滬深指數(shù)的投資組合進(jìn)行Monte Carlo模擬,以度量證券市場(chǎng)的風(fēng)險(xiǎn)。從實(shí)證結(jié)果可以看出,用t-Copula-GARCH-t函數(shù)度量VaR是有效的。 第六章為論文的結(jié)束語(yǔ)。首先總結(jié)全文內(nèi)容,包括論文的原理、方法和模型,然后對(duì)針對(duì)未來(lái)的相關(guān)研究提出了幾種政策建議。 本文的創(chuàng)新之處是建立計(jì)算風(fēng)險(xiǎn)價(jià)值VaR的Copula-GARCH模型,并應(yīng)用到分析滬深指數(shù)投資組合的風(fēng)險(xiǎn)測(cè)度,使VaR的計(jì)量精度大大提高,Monte Carlo模擬法從歷史數(shù)據(jù)出發(fā),利用一系列檢驗(yàn)和統(tǒng)計(jì)方法,找到能較好得刻畫數(shù)據(jù)特征的分布函數(shù),據(jù)此進(jìn)行模擬的效果比較好。 本文的不足之處在于：上證指數(shù)和深證綜指組成的投資組合中,它們相應(yīng)的投資比例是固定的,沒有進(jìn)行動(dòng)態(tài)投資優(yōu)化；由于Copula的估計(jì)相對(duì)較難,二元的Copula模型相對(duì)來(lái)說(shuō)最多,二元的Copula顯然不能很好對(duì)多個(gè)資產(chǎn)進(jìn)行分析。
[Abstract]:With the continuous development of information technology and the global economic and financial integration is accelerating the pace of financial market volatility and risk are also increasing influence on the state and enterprises is also increasing, so that the financial risk management has been valued by financial institutions and investors more and more. To measure the risk analysis technology to develop very quickly. The risk value of VaR (Value at Risk) method originated in 1980s, is a statistical method to measure the risk of financial market, it is widely used in banks, securities companies, commodities and other trade organizations. The main features of this study is to measure and method of value at risk in the financial market risk in the market, has an important position in modern financial risk management in China. The application of VaR method is also gradually developed, there are also many of its research content Its main contents are: (1) predict the possible financial loss risk measurement in the future. (2) identify the distribution of risk factors and how to affect the distribution of portfolio. (3) provide reliable information for portfolio optimization and risk management.
The traditional method of VaR measurement has some shortcomings, such as the normal distribution assumption, does not meet the consistency axiom, lack of extreme events and the tail correlation between financial assets is considered. These will affect the portfolio investment risk and effect, effect of VaR measurement accuracy. And the theory of Copula relative to the traditional method there is a theoretical advantage, Copula model can study the organic correlation and the correlation pattern together. Through the Copula function can capture the correlation between nonlinear, asymmetric and tail correlation theory of.Copula for multivariate financial time series analysis provides a new way of thinking.
The main idea of this paper is to consider the phenomenon in the financial market volatility, establish GARCH model to measure the risk of financial market, and some disadvantages of VaR method, introducing the Copula function, the marginal distribution of the fat tail phenomenon in financial data variables are assumed to obey normal distribution, t distribution, GED distribution through the failure rate test and K-S test to determine the marginal distribution model and the optimal Copula function optimal. The optimal marginal distribution of GARCH model and the optimal combination of Copula function, through the Monte Carlo simulation method to measure the Shanghai and Shenzhen index portfolio VaR.
This article is divided into six parts, the main contents are as follows:
The first chapter is the introduction. It briefly summarizes the background and significance of the research, summarizes the research status of VaR at home and abroad, and the situation of using Copula to measure VaR at home and abroad. Finally, it gives a brief description of the research contents, methods, innovations and shortcomings.
The second chapter is the knowledge of financial risk. The definition of financial risk and the traditional methods for measuring the financial risk are briefly summarized.
The third chapter is the theoretical basis of VaR. Firstly, introduces the background, the concept of VaR VaR theory, the holding period and the confidence level. Secondly, focuses on three kinds of traditional measuring methods of VaR, including the delta normal method, historical simulation, Monte Carlo simulation method. Then, the model and method of calculation of VaR test is given. Finally the advantages and disadvantages of the VaR to do a simple introduction.
The fourth chapter is the Copula function related knowledge. A detailed explanation of the definition of Copula function and related properties, introduces the common functions and related theorems of Copula. The simple description of correlation using Copula function measurement Kendall's tau and Spearman's at the same time, the tail correlation are briefly described. Finally expounded the calculation method VaR Copula-GARCH model Monte based on the method of Carlo simulation.
The fifth chapter is the empirical analysis part of this paper. This paper constructs a portfolio for the Shanghai index and Shenzhen composite index according to the right combination, time is 2008.1.1-2013.4.3, a total of 1278 sets of data.
Take the empirical analysis log returns, first estimates the marginal distribution model, through the analysis of test data characteristics, volatility, stationarity, autocorrelation and partial autocorrelation and ARCH effect were in normal distribution, t distribution, GED distribution was measured by regression, further analysis, to determine the optimal model for GARCH (1,1) -t model.
Then the t-Copula function to describe the correlation between variables of the appropriate Copula function through the K-S test, and the result is verified by using rank correlation coefficient. In this paper the t-Copula function based on the GARCH-t model of the Shanghai and Shenzhen index portfolio of Monte Carlo simulation, to measure the risk of securities market. The empirical results show that using the t-Copula-GARCH-t function to measure VaR effective.
The sixth chapter is the concluding remarks of the paper. First, we summarize the contents of the paper, including the principles, methods and models of the paper, and then put forward several policy recommendations for future research.
The innovation of this paper is to establish the Copula-GARCH model to calculate the risk value of VaR, and applied to the analysis of the Shanghai and Shenzhen index portfolio risk measure, the measurement accuracy of VaR is greatly improved, Monte Carlo simulation method based on historical data, using a series of test and statistical method, the distribution function can find better characterization of data characteristics, accordingly the simulation result is good.
The inadequacies of this article are: Shanghai index and Shenzhen composite index portfolio, their corresponding investment ratio is fixed, there are no dynamic investment optimization; because the Copula estimation is relatively difficult, the Copula model is relatively the most two yuan two yuan Copula, obviously not good for multiple assets analysis.

【學(xué)位授予單位】：東北財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F830.9;F224

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