本文選題：單因子Copula模型 + 嵌套Copula模型�。� 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：在改革開放進(jìn)一步深化和經(jīng)濟(jì)發(fā)展的不斷推動(dòng)下,我國(guó)金融市場(chǎng)逐步發(fā)展健全和完善,金融市場(chǎng)之間的依賴性和金融資產(chǎn)的價(jià)格協(xié)同效應(yīng)愈來(lái)愈顯著,其中股票市場(chǎng)作為金融市場(chǎng)的重要組成部分,不同市場(chǎng)、不同板塊、不同行業(yè)以及不同股票之間常常存在著聯(lián)動(dòng)效應(yīng),某一市場(chǎng)或資產(chǎn)的波動(dòng),經(jīng)常會(huì)引起其他市場(chǎng)或資產(chǎn)的波動(dòng),導(dǎo)致風(fēng)險(xiǎn)會(huì)迅速波及、傳染、放大至其他市場(chǎng)或資產(chǎn)。隨著我國(guó)股票市場(chǎng)的深入發(fā)展,不同上市公司之間的聯(lián)系和依賴越來(lái)越強(qiáng),公司股票之間的關(guān)聯(lián)性也越來(lái)越明顯,對(duì)我國(guó)大型上市公司股票收益之間的關(guān)聯(lián)性和投資風(fēng)險(xiǎn)進(jìn)行分析,對(duì)投資組合構(gòu)建、市場(chǎng)風(fēng)險(xiǎn)管理乃至股市的健康發(fā)展都有著十分重要的意義。本文基于Copula理論基礎(chǔ),利用因子Copula模型和結(jié)構(gòu)因子Copula模型中的嵌套Copula模型,分析了以滬深300成分股為代表的我國(guó)大型上市公司股票的收益率序列,計(jì)算得到了不同行業(yè)內(nèi)每對(duì)股票收益之間的Spearman秩相關(guān)系數(shù)、相依尾部加權(quán)測(cè)度和不同資產(chǎn)組合的VaR和ES,以此分析了不同行業(yè)內(nèi)各公司股票收益的關(guān)聯(lián)性和投資組合風(fēng)險(xiǎn),以及以全部滬深300成分股為代表的整個(gè)市場(chǎng)的投資組合風(fēng)險(xiǎn)。本文選取了滬深300成分股近5年的日對(duì)數(shù)收益率序列,剔除上市時(shí)間不滿5年的股票,利用兩階段極大似然估計(jì)法,首先采用GARCH(1,1)-Gaussian模型、GARCH(1,1)-t模型分別對(duì)每只股票收益率序列進(jìn)行擬合,并用AIC信息準(zhǔn)則選擇擬合效果較好的模型,經(jīng)過(guò)對(duì)標(biāo)準(zhǔn)殘差序列的K-S檢驗(yàn)和Ljung-Box自相關(guān)檢驗(yàn)發(fā)現(xiàn),GARCH(1,1)-Gaussian模型、GARCH(1,1)-t模型可以較好的擬合各收益率序列的邊緣分布,并且利用單因子Copula模型對(duì)各公司股票收益的標(biāo)準(zhǔn)殘差序列進(jìn)行擬合,發(fā)現(xiàn)在所有17個(gè)二級(jí)行業(yè)中,保險(xiǎn)、材料、地產(chǎn)、能源、汽配、食品飲料、銀行、運(yùn)輸、資本市場(chǎng)等9種行業(yè)的股票收益序列擬合效果較好的為單因子BB1 Copula模型,公用、零售、媒體、耐用服裝、軟件、硬件、制藥生物、資本品等8種行業(yè)的股票收益序列擬合效果較好的為單因子Rotated Gumbel Copula模型;同時(shí)本文利用結(jié)構(gòu)因子Copula模型中的嵌套Frank Copula模型,對(duì)17個(gè)行業(yè)的全部股票收益殘差序列進(jìn)行了擬合,并得到了相關(guān)模型參數(shù)。通過(guò)極大似然估計(jì)得到相關(guān)參數(shù)后,本文利用單因子Copula模型計(jì)算了每個(gè)行業(yè)內(nèi)不同股票收益序列之間的Spearman秩相關(guān)系數(shù)、相依尾部加權(quán)測(cè)度,分別分析其相關(guān)性和尾部相關(guān)性,發(fā)現(xiàn)大部分配對(duì)股票收益的秩相關(guān)系數(shù)在0.3到0.8的區(qū)間內(nèi),呈明顯的中度正相關(guān),每個(gè)行業(yè)內(nèi)部的整體相關(guān)性也呈明顯的中度正相關(guān),其中資本市場(chǎng)、保險(xiǎn)、能源、運(yùn)輸?shù)刃袠I(yè)的內(nèi)部整體關(guān)聯(lián)性較強(qiáng),食品飲料、硬件等行業(yè)整體關(guān)聯(lián)性較弱;在尾部相關(guān)性方面,通過(guò)對(duì)各行業(yè)內(nèi)所有配對(duì)成分股相依尾部加權(quán)測(cè)度的均值分析,發(fā)現(xiàn)各行業(yè)內(nèi)部均呈正的尾部相關(guān)性,除銀行業(yè)的上下尾部加權(quán)測(cè)度均值一致外,其余行業(yè)均表現(xiàn)為下尾相關(guān)性強(qiáng)于上尾相關(guān)性,其中銀行業(yè)和資本市場(chǎng)業(yè)的上下尾部加權(quán)測(cè)度均值均高于0.6,說(shuō)明兩個(gè)行業(yè)內(nèi)部的整體尾部相關(guān)性均較強(qiáng)。最后,本文利用單因子Copula模型和嵌套Frank Copula模型,采取蒙特卡洛仿真技術(shù),計(jì)算每個(gè)行業(yè)等權(quán)重投資組合和整個(gè)市場(chǎng)等權(quán)重投資組合的VaR和ES,分析其投資風(fēng)險(xiǎn),發(fā)現(xiàn)在置信水平分別為99%、97.5%、95%的情況下,資產(chǎn)組合的VaR和ES較大的行業(yè)為零售業(yè)、銀行業(yè)、硬件業(yè)、資本市場(chǎng)業(yè)、制藥生物業(yè)、保險(xiǎn)業(yè)等,其中零售行業(yè)由于只有兩只成分股,分散程度低,整體風(fēng)險(xiǎn)明顯高于其他行業(yè),而對(duì)于整個(gè)市場(chǎng)的資產(chǎn)組合,由于分散程度較高,在不同的置信度下,VaR和ES均明顯較低,投資風(fēng)險(xiǎn)明顯低于單個(gè)行業(yè)的資產(chǎn)組合。
[Abstract]:Continue to promote the further deepen reform and opening up and economic development, China's financial market is gradually improving and perfecting the financial market development, the dependence between financial asset prices and increasingly significant synergies, including stock market as an important part of the financial market, the different markets, different sectors, different industries and between different stocks often there is a linkage effect, a market or asset volatility, will often lead to other markets or asset volatility, risk will quickly spread, infection, enlarged to other markets or assets. With the further development of China's stock market, listed companies between different links and rely on more and more strong, the company stock Association the more and more obvious, to China's large stock return correlation and investment risk analysis, portfolio construction, market risk management Have a very important significance and the healthy development of the stock market. Based on Copula theory, using the nested Copula model Copula model factor and structure factor Copula model, analyzes the CSI 300 stocks as the representative of China's large-scale listed stock return series, the Spearman rank correlation coefficient between different industries within each of the stock return calculation, dependent tail weighted measure and different portfolio of VaR and ES, analyzed the different industries within the company stock return correlation and portfolio risk, and the whole market with full CSI 300 stocks as the representative of the investment portfolio risk. This paper chooses the CSI 300 stocks daily log return rate series of nearly 5 years, excluding the listed stock time of less than 5 years, the maximum likelihood estimation method using two stages, firstly using GARCH (1,1) -Gaussian model, GARCH (1 1, -t) model respectively for each stock return series fitting, choose a good fitting effect model and AIC information criterion, through the test of K-S and Ljung-Box on the standard residual autocorrelation test showed that GARCH (1,1) -Gaussian GARCH (1,1) model, -t model can better fit the rate of return the sequence of marginal distribution, and using single factor Copula model standard error on the stock returns of each company sequence fitting, found in all 17 two industries, insurance, energy, materials, real estate, auto parts, food and beverage, banking, transportation, capital market and other 9 kinds of industry stock returns better fitting effect single factor BB1 Copula model, the public, media, retail, durable clothing, hardware, software, bio pharmaceutical industry, capital goods and other 8 kinds of stock return series of good fitting effect for the single factor Rotated Gumbel Copula model; at the same time Using nested Frank Copula model structure factor Copula model, all the stock return residuals of 17 industry fitting, and relevant model parameters. The relevant parameters obtained by maximum likelihood estimation, using the single factor Copula model Spearman rank correlation coefficient between different stock returns within each industry were calculated. The tail dependence weighted measure to analyze the correlation and tail dependence, respectively, found that most pairs of stock yield rank correlation coefficient in the range of 0.3 to 0.8, showed a moderate positive correlation showed a moderate positive correlation between the overall each within the industry, including capital markets, insurance, energy, transportation and other industries inside the overall association is strong, food and beverage, hardware and other industries overall weak associations; at the end of the correlation, the industry in a paired component Analysis of mean weighted measure of tail dependence, found the tail correlation within the industry positively, in addition to the banking industry on the lower tail weighted measure mean the same, the rest of the industry showed a lower tail correlation is stronger than the upper tail dependence, including banking and capital markets in the tail of weighted measure was higher than that in 0.6. The overall internal tail correlation of two industries are strong. Finally, using the single factor Copula model and the nested Frank Copula model, adopt the Monte Carlo simulation, calculate the weight of each industry portfolio and the market portfolio weights of VaR and ES, to analyze the investment risk, found in the confidence level were 99%, 97.5% 95%, under the condition that the assets of the combination of VaR and ES is larger for the retail industry, banking industry, hardware industry, pharmaceutical industry capital market, property insurance, health, the retail industry as a result of Only two constituent stocks, low dispersion, the overall risk is significantly higher than other industries, and for the entire market portfolio, due to the higher dispersion, VaR and ES are significantly lower under different confidence levels, and the investment risk is significantly lower than the single industry portfolio.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51

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