本文選題：波動(dòng)性　切入點(diǎn)：相關(guān)性　出處：《首都經(jīng)濟(jì)貿(mào)易大學(xué)》2017年碩士論文

【摘要】：近些年來,隨著股票市場(chǎng)不斷發(fā)展,眾多關(guān)于股市波動(dòng)性和相關(guān)性以及關(guān)于金融資產(chǎn)投資組合最優(yōu)選擇的理論也逐漸得到完善與改進(jìn)。為了研究股票市場(chǎng)波動(dòng)性及相關(guān)性的問題,本文選取了上海證券交易所公布的上證行業(yè)指數(shù)數(shù)據(jù)作為研究對(duì)象,分別假定殘差擾動(dòng)項(xiàng)服從正態(tài)分布、廣義誤差分布、偏態(tài)廣義誤差分布,對(duì)數(shù)據(jù)建立GARCH(1,1)模型。隨后,文章在GARCH模型中引入馬爾科夫機(jī)制轉(zhuǎn)換過程,建立兩狀態(tài)MS-GARCH模型,通過模型估計(jì)結(jié)果顯示上證行業(yè)指數(shù)存在著低波動(dòng)和高波動(dòng)兩種狀態(tài),且處于低波動(dòng)的期望持續(xù)時(shí)間較長(zhǎng)。通過模型的對(duì)比發(fā)現(xiàn),GARCH模型殘差擾動(dòng)項(xiàng)服從偏態(tài)廣義誤差分布的擬合效果比其他分布的擬合效果好;MS-GARCH模型對(duì)波動(dòng)性的描述比GARCH模型更好。之后,對(duì)上證行業(yè)指數(shù)正常狀態(tài)、低波動(dòng)狀態(tài)、高波動(dòng)狀態(tài)分別進(jìn)行了動(dòng)態(tài)相關(guān)系數(shù)的計(jì)算,以上證能源為例,結(jié)果表明上證能源與其他九個(gè)行業(yè)呈現(xiàn)較高的正相關(guān)性,而且在2015年至2016年之間的正相關(guān)性最高;經(jīng)過狀態(tài)轉(zhuǎn)換后的兩狀態(tài)相關(guān)性有著明顯降低。最后對(duì)相關(guān)性最高時(shí)間段內(nèi)的數(shù)據(jù)建立均值-方差模型,通過計(jì)算得到最優(yōu)前沿,發(fā)現(xiàn)通過增減上證電信的投資比例能直接影響到投資組合的收益。綜上所述,我國(guó)的上證行業(yè)指數(shù)具有高波動(dòng)性和高相關(guān)性的特點(diǎn),因此在做投資組合最優(yōu)選擇時(shí)應(yīng)密切關(guān)注收益率變化和股票市場(chǎng)的相關(guān)信息。
[Abstract]:In recent years, with the development of the stock market, Many theories about volatility and correlation of stock market and optimal choice of financial asset portfolio have been perfected and improved gradually in order to study the problem of volatility and correlation in stock market. In this paper, the Shanghai Stock Exchange industry index data published by the Shanghai Stock Exchange are selected as the research objects. The residual disturbance terms are assumed to follow normal distribution, generalized error distribution and skewness generalized error distribution respectively. In this paper, the Markov mechanism transformation process is introduced into the GARCH model, and a two-state MS-GARCH model is established. The results of the model estimation show that there are two states of low volatility and high volatility in the index of Shanghai Stock Exchange. Through the comparison of the models, it is found that the fitting effect of GARCH model is better than that of other distributions, and that the MS-GARCH model describes the volatility better than the other distributions. The GARCH model is better. After that, The dynamic correlation coefficient is calculated for the normal state, low fluctuation state and high fluctuation state of Shanghai Stock Exchange industry index. Taking Shanghai Stock Exchange Energy as an example, the results show that Shanghai Stock Exchange Energy has a high positive correlation with other nine industries. Moreover, the positive correlation between 2015 and 2016 is the highest; after the state transition, the correlation between the two states is significantly reduced. Finally, the mean-variance model is established for the data in the period of the highest correlation, and the optimal frontier is obtained by calculation. It is found that the increase or decrease of the investment ratio of Shanghai Telecom can directly affect the return of the investment portfolio. To sum up, the Shanghai Stock Exchange industry index in China has the characteristics of high volatility and high correlation. Therefore, we should pay close attention to the change of return rate and the relevant information of stock market.
【學(xué)位授予單位】：首都經(jīng)濟(jì)貿(mào)易大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224;F832.51

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