本文選題：主板 + 創(chuàng)業(yè)板��； 參考：《北京工商大學(xué)》2014年碩士論文

【摘要】：2009年10月30日，創(chuàng)業(yè)板市場(chǎng)首批28家上市公司在深圳交易所正式掛牌交易，標(biāo)志著我國(guó)創(chuàng)業(yè)板時(shí)代正式來(lái)臨。與主板市場(chǎng)相比，創(chuàng)業(yè)板市場(chǎng)在上市企業(yè)規(guī)模、行業(yè)分布、投資者結(jié)構(gòu)、退市制度等方面存在較大的區(qū)別，但兩個(gè)市場(chǎng)均屬于我國(guó)多層次證券市場(chǎng)的一部分，都要面臨一些共同的影響因素，那么兩個(gè)市場(chǎng)的走勢(shì)是否相關(guān)，如果存在相關(guān)，那么在不同情形下相關(guān)程度如何，比如在暴跌或暴漲的情形下相關(guān)程度是否提高，這種“尾部相關(guān)性”，對(duì)于投資者和風(fēng)險(xiǎn)管理者是一個(gè)至關(guān)重要的問(wèn)題。因此，本文對(duì)主板和創(chuàng)業(yè)板之間的相關(guān)結(jié)構(gòu)進(jìn)行深入研究。本文采用Copula函數(shù)技術(shù)對(duì)兩個(gè)市場(chǎng)的相關(guān)結(jié)構(gòu)進(jìn)行分析，并且結(jié)合風(fēng)險(xiǎn)價(jià)值VaR進(jìn)行風(fēng)險(xiǎn)預(yù)測(cè)能力分析。 首先，本文分析了我國(guó)主板和創(chuàng)業(yè)板市場(chǎng)收益率各自的分布特征。在Q-Q圖等檢驗(yàn)基礎(chǔ)上，通過(guò)對(duì)風(fēng)險(xiǎn)價(jià)值VaR的預(yù)測(cè)能力進(jìn)行比較，發(fā)現(xiàn)GARCH-Normal模型不能很好的捕捉創(chuàng)業(yè)板市場(chǎng)的尾部特征，而較好的模型為GARCH-T模型。但在主板市場(chǎng)上，相比GARCH-T模型，簡(jiǎn)單的GARCH-Normal能夠更準(zhǔn)確的捕捉尾部特征。即與主板市場(chǎng)相比，創(chuàng)業(yè)板市場(chǎng)存在更厚的尾部。 其次，在對(duì)主板和創(chuàng)業(yè)板市場(chǎng)收益率分布特征有效估計(jì)的基礎(chǔ)上，對(duì)不同Copula函數(shù)進(jìn)行估計(jì)。通過(guò)檢驗(yàn)發(fā)現(xiàn)，混合Copula模型更適合用來(lái)描述我國(guó)主板和創(chuàng)業(yè)板市場(chǎng)之間的相關(guān)結(jié)構(gòu)，兩個(gè)市場(chǎng)之間整體具有正相關(guān)關(guān)系，但存在非對(duì)稱的尾部相關(guān)，即兩個(gè)市場(chǎng)在暴漲或暴跌的情況下，相關(guān)程度明顯提高，并且在暴跌時(shí)的相關(guān)程度高于暴漲時(shí)的相關(guān)程度。并在Copula基礎(chǔ)下，利用蒙特卡洛模擬方法對(duì)投資組合的VaR進(jìn)行分析，發(fā)現(xiàn)無(wú)論在樣本內(nèi)還是樣本外混合Copula的風(fēng)險(xiǎn)預(yù)測(cè)效果最優(yōu)。
[Abstract]:On October 30, 2009, the first batch of 28 listed companies in the gem market were officially listed on the Shenzhen Stock Exchange, marking the formal arrival of the gem era in China. Compared with the main market, the gem market has great differences in the scale of listed enterprises, industry distribution, investor structure, delisting system and so on. However, the two markets are part of the multi-level securities market in China. All have to face some common influencing factors. Well, is the trend of the two markets relevant? if there is a correlation, what is the degree of correlation in different situations, for example, whether the correlation degree is increased in the case of a slump or a surge? This tail correlation is a crucial issue for investors and risk managers. Therefore, this article carries on the thorough research to the main board and the growth enterprise board correlation structure. In this paper, the Copula function technique is used to analyze the related structure of the two markets, and the risk forecasting ability is analyzed by combining the risk value VaR. First of all, this paper analyzes the distribution characteristics of the return rate of China's main board and gem market. On the basis of Q-Q chart and other tests, it is found that the GARCH-Normal model can not capture the tail characteristics of gem market well, and the better model is GARCH-T model by comparing the prediction ability of VaR with risk value. But in the main board market, compared with the GARCH-T model, the simple GARCH-Normal can capture the tail features more accurately. That is, compared with the main market, gem market has a thicker tail. Secondly, on the basis of the efficient estimation of the return distribution characteristics of the main board and the growth enterprise market, different Copula functions are estimated. It is found that the hybrid Copula model is more suitable to describe the correlation structure between the main board and the gem market in China. There is a positive correlation between the two markets, but there is an asymmetric tail correlation between the two markets. That is, the correlation between the two markets in the case of a sharp rise or fall, significantly increased, and the correlation in the collapse is higher than the correlation in the skyrocketing. On the basis of Copula, Monte Carlo simulation method is used to analyze the VaR of the portfolio, and it is found that the risk prediction effect of mixed Copula in and out of the sample is the best.
【學(xué)位授予單位】：北京工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.51;F224

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