本文關(guān)鍵詞：基于Copula模型的股票與債券投資組合策略研究　出處：《首都經(jīng)濟(jì)貿(mào)易大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 投資組合 靜態(tài)Copula函數(shù) 時(shí)變Copula函數(shù) 藤結(jié)構(gòu)Copula函數(shù)

【摘要】：金融市場(chǎng)風(fēng)云變幻,2008年以來中國(guó)經(jīng)歷了兩次比較大的經(jīng)濟(jì)波動(dòng),也反射出證券市場(chǎng)的系統(tǒng)性風(fēng)險(xiǎn)和指標(biāo)監(jiān)控的匱乏。對(duì)于投資者來說,如何在這樣的市場(chǎng)環(huán)境中規(guī)避風(fēng)險(xiǎn),獲取收益是最關(guān)注的問題,在我國(guó)股票市場(chǎng)和債券市場(chǎng)的分割較為明顯,但是兩個(gè)市場(chǎng)又同時(shí)組成了資本市場(chǎng)的兩大支柱,所以研究?jī)烧咧g的關(guān)聯(lián)關(guān)系,并且從投資組合的角度對(duì)投資者做出建議是十分具有現(xiàn)實(shí)意義的。隨著研究的加深,研究者發(fā)現(xiàn)市場(chǎng)之間并不是簡(jiǎn)單的Granger因果關(guān)系或者線性相關(guān)關(guān)系,存在著“非正態(tài)性”“非對(duì)稱性”“尖峰厚尾”和“波動(dòng)聚集性”等特點(diǎn),而Copula函數(shù)由于其對(duì)邊緣分布沒有要求,可以直接對(duì)相關(guān)關(guān)系進(jìn)行建模等優(yōu)勢(shì),在對(duì)金融時(shí)間序列相關(guān)性的刻畫上發(fā)揮著重大的作用。在理論方面,本文描述分析了五種靜態(tài)Copula函數(shù)、一種混合Copula函數(shù),三種藤結(jié)構(gòu)Copula函數(shù)以及三種動(dòng)態(tài)Copula函數(shù)的定義和性質(zhì),并且介紹了風(fēng)險(xiǎn)度量的相關(guān)理論。在實(shí)證方面,本文選取上證指數(shù)、深證綜合指數(shù)和中證債券綜合指數(shù)為研究對(duì)象,上證指數(shù)和深證綜合指數(shù)代表股票市場(chǎng)趨勢(shì)和波動(dòng),中證債券綜合指數(shù)是衡量債券市場(chǎng)趨勢(shì)和波動(dòng)的重要指數(shù),由于影響的滯后效應(yīng)和保證數(shù)據(jù)的連續(xù)性,本文未對(duì)數(shù)據(jù)非同期進(jìn)行刪減,首先對(duì)兩個(gè)市場(chǎng)2008年來的發(fā)展現(xiàn)狀和波動(dòng)情況進(jìn)行了定性的描述,之后用GARCH(0,4)-t模型來擬合上證指數(shù)的邊緣分布,用GARCH(3,0)-t模型來擬合中證債券指數(shù)的邊緣分布,用GARCH(1,1)-t模型來擬合深證指數(shù)的邊緣分布,在驗(yàn)證股票市場(chǎng)和債券市場(chǎng)相依性方面,本文選取三種指數(shù)通過五種靜態(tài)模型和三種藤結(jié)構(gòu)Copula函數(shù)來驗(yàn)證,并對(duì)模型擬合效果進(jìn)行分析;在對(duì)動(dòng)態(tài)模型性質(zhì)的研究中,我們選擇上證指數(shù)和深證綜合指數(shù),并用三種時(shí)變模型并和一種靜態(tài)混合Copula模型來擬合,對(duì)比分析選擇出擬合效果最好的模型,最后實(shí)現(xiàn)Copula函數(shù)和風(fēng)險(xiǎn)價(jià)值的結(jié)合,根據(jù)歷史收益率,通過蒙特卡羅方法隨機(jī)產(chǎn)生服從GARCH-t邊緣分布和Copula聯(lián)合分布的隨機(jī)數(shù)列,計(jì)算等比重下的Va R和CVaR的值,通過失敗率檢驗(yàn)的方法進(jìn)行檢驗(yàn)擬合的效果,并通過10000次的模擬進(jìn)行投資組合的優(yōu)化,選擇出最優(yōu)的投資組合。通過研究發(fā)現(xiàn),在靜態(tài)Copula中,相比橢圓類Copula函數(shù),阿基米德Copula函數(shù)更能擬合收益率序列的“非對(duì)稱性”和“尖峰后尾”的特征,而三種藤結(jié)構(gòu)的Copula函數(shù)擬合的效果優(yōu)于任何單一的靜態(tài)模型。在對(duì)上證指數(shù)和深證指數(shù)進(jìn)行靜態(tài)混合Copula模型和時(shí)變SJC Copula模型的擬合后,發(fā)現(xiàn)時(shí)變SJC Copula模型更能衡量出時(shí)間序列之間不斷變化的相依關(guān)系;基于藤結(jié)構(gòu)Copula模型研究了股票和債券市場(chǎng)的最優(yōu)配比,并對(duì)比了Copula-mean-Variance模型和mean-CVa R模型,驗(yàn)證了CVa R在衡量風(fēng)險(xiǎn)上的優(yōu)越性。同時(shí)可以看到隨著對(duì)于期望收益率的提高,最優(yōu)的投資組合會(huì)向股票市場(chǎng)發(fā)生偏移,而且具有厚尾特征的股票的占比就會(huì)增大。
[Abstract]:The financial market since 2008 has experienced China amidst the winds of change, two large economic fluctuations, reflecting the lack of systematic risk of the securities market and index control. For investors, how to avoid the risk in such a market environment, income is the most concern in China's stock market and bond market segmentation the more obvious, but the two markets and formed the two pillars of the capital market, so the study of relationship between the two, is of great practical significance and from the perspective of portfolio for investors to make recommendations. As the research deepened, the researchers found that between the market is not a simple causal relationship or linear correlation Granger there is a relationship, "non normal" "non symmetry" "fat tail" and "volatility clustering" and other characteristics, and the Copula function because of its marginal distribution No, it can directly model the advantages of correlation, play a significant role in the portrayal of the correlation of financial time series. In theory, this paper describes and analyzes the five kinds of static Copula function, a hybrid Copula function, three kinds of rattan structure Copula function and three kinds of dynamic Copula function definition and the nature, and introduces the theory of risk measure. In the empirical analysis, this paper selects the Shanghai index and Shenzhen Composite Index and comprehensive index card bonds as the research object, the Shanghai Composite Index and Shenzhen composite index represents the stock market trend and volatility, CSI bond index is an important index to measure the bond market trend and volatility, because the influence of continuous lag effect and ensure the data, the data of non simultaneous deletion, firstly, the development status and the fluctuation of the two markets of 2008 A qualitative description, then use GARCH (0,4) -t model to fit the marginal distribution of the Shanghai index, GARCH (3,0) -t model to fit the marginal distribution of CSI bond index, GARCH (1,1) -t model to fit the marginal distribution of Shenzhen stock index, in the verification of the stock market and bond market dependence. This paper selects three index by five static models and three kinds of rattan structure Copula function to verify, and the model fitting effect is analyzed; in the research on the dynamic properties of the model, we select the Shanghai index and Shenzhen composite index, and with three kinds of time-varying model and a static Copula model to fit. Comparative analysis of selection of the best fitting effect of the model, finally using the Copula function and the value at risk, according to the historical return, by the method of Monte Carlo random edge obeys GARCH-t distribution and Copula joint distribution of random numbers The calculation of the proportion of Va, R and CVaR, to test the fitting effect through the method of failure rate test, and optimize the investment portfolio through 10000 times of simulation, select the optimal portfolio. The study found that in static Copula, compared to Copula elliptic function, Archimedes function Copula the fitting characteristics of the return series of "non symmetry" and "tail", and the effect of three kinds of Copula function fitting structure of the vine is superior to any single static model. SJC Copula model in fitting of Shanghai stock index and Shenzhen stock index for static Copula model and the time varying SJC Copula, found the model can measure more dependent changing relationship between time series; the optimal proportion of rattan structure based on Copula model of stock and bond market, and compares the Copula-mean-Variance model and mean-CVa R The model validates the superiority of CVa R in measuring risks. At the same time, we can see that with the increase of expected yield, the optimal portfolio will shift to the stock market, and the proportion of stocks with thick tail will increase.

【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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