本文關(guān)鍵詞： 創(chuàng)業(yè)板指數(shù) 聯(lián)動(dòng)性分析 GARCH類模型 VAR　出處：《湖南大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：創(chuàng)業(yè)板市場主要是用來解決中小型企業(yè)的融通資金困難問題的，幫助和支持中小型企業(yè)，尤其是成長性較高的新興企業(yè)。對(duì)于中國這樣一個(gè)經(jīng)濟(jì)高速發(fā)展的國家，中小企業(yè)的成長和發(fā)展更為棘手。因此，將高成長、具有高新技術(shù)的公司引入市場，可以活躍我國的證券市場，同時(shí)，可以為投資者提供更多的投資機(jī)會(huì)，給市場競爭帶來活力，也可以提高上市公司本身的競爭力。 我國創(chuàng)業(yè)板市場主要由高成長性的中小企業(yè)組成的，企業(yè)面臨的風(fēng)險(xiǎn)較大，從而使創(chuàng)業(yè)板指數(shù)的波動(dòng)性較大。分析結(jié)果顯示在5%的顯著性水平下，創(chuàng)業(yè)板指數(shù)變化和上證綜指互為格蘭杰因果關(guān)系，與深成指數(shù)也互為格蘭杰因果關(guān)系，同時(shí)進(jìn)一步研究了創(chuàng)業(yè)板市場與上證、深證之間的動(dòng)態(tài)關(guān)聯(lián)關(guān)系。 本文著重利用GARCH模型及其推廣模型建立創(chuàng)業(yè)板市場的波動(dòng)模型來刻畫創(chuàng)業(yè)板市場的波動(dòng)情況；接著針對(duì)金融資產(chǎn)的尖峰厚尾的特征，引入了能刻畫收益率尖峰厚尾特征的廣義誤差分布（GED分布），分別利用正態(tài)分布、t分布、GED分布擬合創(chuàng)業(yè)板指數(shù)收益率的分布，，對(duì)比驗(yàn)證三種分布擬合的精準(zhǔn)度，達(dá)到更精準(zhǔn)地刻畫金融資產(chǎn)波動(dòng)性的要求。 最后對(duì)創(chuàng)業(yè)板市場的風(fēng)險(xiǎn)進(jìn)行度量，來比較不同波動(dòng)模型下的風(fēng)險(xiǎn)度量值。通過對(duì)在不同置信水平、不同分布、不同GARCH模型下計(jì)算的VaR值進(jìn)行比較并得出：t分布下和99%的置信水平下，容易高估風(fēng)險(xiǎn)；在GED分布下EGARCH-VaR模型對(duì)風(fēng)險(xiǎn)的覆蓋程度較好。在實(shí)際的投資決策過程中，無論風(fēng)險(xiǎn)被高估還是被低估，都不利于決策者對(duì)風(fēng)險(xiǎn)進(jìn)行有效的管理,因此，對(duì)創(chuàng)業(yè)板市場的波動(dòng)性風(fēng)險(xiǎn)進(jìn)行準(zhǔn)確的評(píng)估可以有效地管理創(chuàng)業(yè)板市場的風(fēng)險(xiǎn)。
[Abstract]:The gem market is mainly used to solve the financing difficulties of small and medium-sized enterprises. It helps and supports small and medium-sized enterprises, especially the new ones with relatively high growth. For a country like China, which has a high economic growth rate, The growth and development of small and medium-sized enterprises are more difficult. Therefore, introducing high-growth, high-tech companies into the market can activate our securities market and at the same time, can provide more investment opportunities for investors. Market competition to bring vitality, but also to improve the competitiveness of listed companies themselves. The gem market in China is mainly composed of small and medium-sized enterprises with high growth. The enterprises are facing greater risks, thus making the gem index more volatile. The analysis results show that under the significant level of 5%, The change of the gem index and the Shanghai Composite Index are Granger causality, and the Shenzhen Composite Index is also the Granger causality. At the same time, the dynamic relationship between the gem market and the Shanghai Stock Exchange and the Shenzhen Stock Exchange is further studied. In this paper, we use GARCH model and its extension model to establish the volatility model of gem market to describe the volatility of gem market, and then focus on the characteristics of financial assets peak and thick tail. The generalized error distribution (GED), which can describe the characteristics of the peak and thick tail of yield, is introduced. The distribution of growth enterprise board index yield is fitted by normal distribution / t distribution and GED distribution, respectively, and the accuracy of the fitting of the three distributions is compared and verified. To achieve a more accurate characterization of the volatility of financial assets requirements. Finally, the risk of gem market is measured to compare the risk measures under different volatility models. The calculated VaR values under different GARCH models are compared and it is found that the risk is easily overestimated under the ratio t distribution and the confidence level of 99%, and the EGARCH-VaR model has a better coverage of risk under the GED distribution. Whether the risk is overestimated or undervalued, it is unfavorable for the decision-makers to manage the risk effectively. Therefore, the accurate assessment of the volatility risk of the gem market can effectively manage the risk of the gem market.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51

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