本文選題：滬深股市　切入點(diǎn)：收益率分布　出處：《天津財(cái)經(jīng)大學(xué)》2013年碩士論文

【摘要】：股指的波動(dòng)特征無外乎單個(gè)股指的波動(dòng)特征與多個(gè)股指間的波動(dòng)特征,關(guān)于單個(gè)股指波動(dòng)特征的研究,即單個(gè)股指收益率分布的研究,本文基于非參數(shù)核密度估計(jì)方法,充分利用樣本數(shù)據(jù)本身特征來擬合股指收益率密度曲線,有效降低了參數(shù)模型中因既定假設(shè)分布所引起的擬合誤差；關(guān)于多個(gè)股指間波動(dòng)特征的研究,即不同股指間波動(dòng)相關(guān)性的研究,本文使用連接函數(shù)與核密度估計(jì)相結(jié)合,即在不假設(shè)邊緣分布的條件下,利用半?yún)?shù)方法求解連接函數(shù)中的參數(shù),有效提高了擬合精度。 在以上研究方法體系之上,本文對中國滬深股市收益率及其相關(guān)性進(jìn)行了不同層次與時(shí)間段的實(shí)證分析,得出相關(guān)結(jié)論并提出政策建議。 股指收益率方面,將大盤、板塊及個(gè)股三者收益率分布情況聯(lián)系起來分析,得出結(jié)論：核密度估計(jì)可以靈活準(zhǔn)確擬合股指收益率曲線；宏觀、中觀、微觀三層面的收益率分布情況均為非正態(tài),存在尖峰厚尾；宏觀、中觀收益率分布有更為突出的左側(cè)厚尾,說明更多投資者是風(fēng)險(xiǎn)厭惡型,對股市中利空消息表現(xiàn)出更為強(qiáng)烈的協(xié)同效應(yīng)；微觀個(gè)股表現(xiàn)出左右厚尾情況均存在的復(fù)雜現(xiàn)象,說明上市公司的信譽(yù)和行業(yè)背景影響投資者風(fēng)險(xiǎn)偏好。 股指相關(guān)性方面,本文完成了1997年1月至2012年7月滬深兩市大盤指數(shù)相關(guān)性的靜態(tài)分析,并對2008年9月金融危機(jī)前后滬深兩市相關(guān)性特征進(jìn)行了對比分析,得出結(jié)論：上尾更為緊湊的Gumbel Copula函數(shù)基本能夠捕捉滬深兩市相關(guān)結(jié)構(gòu)的特征,即當(dāng)一個(gè)市場指數(shù)出現(xiàn)高收益率時(shí),另一個(gè)市場出現(xiàn)高收益率指數(shù)的可能性也很大,出現(xiàn)低收益率時(shí)雖然也有類似效應(yīng),但不如高收益率時(shí)的范圍區(qū)間廣。由對比分析可知,相關(guān)程度方面,金融危機(jī)后兩市的相關(guān)依存度較危機(jī)前明顯增強(qiáng),說明滬深兩市在整體上熊市相關(guān)依存度更強(qiáng)；相關(guān)結(jié)構(gòu)方面,危機(jī)前后滬深兩市的相關(guān)結(jié)構(gòu)并沒有發(fā)生非常明顯的變化,說明由美國次貸危機(jī)引發(fā)的全球金融危機(jī)對我國股市的沖擊并沒有改變滬深股市整體相關(guān)結(jié)構(gòu)。
[Abstract]:The volatility characteristic of the index finger is not related to the fluctuation characteristic of single index finger and the fluctuation characteristic between multiple index fingers , namely the study of single index fluctuation characteristic , that is , the study of single index yield distribution . Based on the non - parameter kernel density estimation method , the paper uses the characteristic of sample data itself to fit the curve of the yield density curve of stock index , effectively reduces the fitting error caused by the established hypothesis distribution in the parameter model ;
In this paper , we study the relationship between the fluctuation characteristics of multiple index fingers , that is , the correlation between different index fingers . In this paper , the connection function is combined with kernel density estimation , that is , under the condition that the edge distribution is not assumed , the parameters in the connection function are solved by using the semi - parametric method , and the fitting accuracy is effectively improved .

Based on the above research methods , this paper analyzes the yield and its correlation of Shanghai - Shenzhen stock market at different levels and time periods , and draws relevant conclusions and puts forward policy recommendations .

Based on the analysis of the yield distribution of stock index , plate and stock , it is concluded that nuclear density estimation can fit the yield curve of stock index flexibly and accurately .
Macro - , meso - and micro - level yield distributions are non - normal , and there is a peak - thick tail .
Macro - and middle - view yield distributions have a more prominent left - hand thick tail , which shows that more investors are risk - averse and show a stronger synergy in the stock market .
The micro - stock shows the complex phenomenon in the case of left and right thick tail , which indicates that the reputation and the background of the listed company affect the investor ' s risk appetite .

Based on the analysis of the correlation between Shanghai and Shenzhen in Shanghai and Shenzhen from January 1997 to July 2012 , the paper concludes that the more compact Gumbel Copula function can capture the characteristics of the related structures in Shanghai and Shenzhen .
There is no obvious change in the structure of Shanghai and Shenzhen in Shanghai and Shenzhen before and after the crisis , which shows that the impact of the global financial crisis caused by the U.S . subprime crisis on China ' s stock market has not changed the overall related structure of Shanghai and Shenzhen stock market .

【學(xué)位授予單位】：天津財(cái)經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.51;F224

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