本文關(guān)鍵詞：基于貝葉斯厚尾DCC-MSV模型的股市波動溢出效應研究　出處：《湖南大學》2014年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 股票市場 波動溢出效應 厚尾DCC-MSV模型 MCMC算法 DIC準則

【摘要】：20世紀80年代以來，經(jīng)濟全球化與金融一體化在全球范圍內(nèi)不斷推進，極大增強了世界各國金融市場之間的相互依存性，單個金融市場的波動不但受到其自身前期波動影響，還受到其他金融市場前期波動的影響，這就存在著波動溢出效應。而現(xiàn)有關(guān)于金融市場波動溢出效應的研究大多采用GARCH類模型，與GARCH類模型相比，隨機波動模型被認為更加適合金融領(lǐng)域的實際研究。因此，本文構(gòu)建了具有雙向格蘭杰檢驗的貝葉斯厚尾DCC-MSV模型，用于研究不同金融市場之間的波動溢出效應與動態(tài)相關(guān)關(guān)系。 首先，對厚尾DCC-MSV模型進行貝葉斯分析，設(shè)計了模型參數(shù)估計的MCMC抽樣算法，解決了多變量隨機波動模型參數(shù)難以估計的難題。然后，介紹了隨機波動模型參數(shù)收斂性診斷的方法、隨機波動模型比較準則以及波動溢出效應顯著性的檢驗方法。最后，本文選取滬深300指數(shù)、恒生指數(shù)、標準普爾500指數(shù)、英國富時100指數(shù)、日經(jīng)225指數(shù)作為研究對象，分析2005年中國大陸股市股權(quán)分置改革后中國大陸股市、中國香港股市、美國股市、英國股市、日本股市的波動溢出效應與動態(tài)相關(guān)關(guān)系，同時，運用DIC準則對CCC-MSV模型、厚尾CCC-MSV模型、DCC-MSV模型、厚尾DCC-MSV模型進行比較分析。 研究結(jié)果表明，在波動溢出效應方面，僅存在中國香港股市對中國大陸股市的單向波動溢出效應，美國股市、英國股市與日本股市對中國大陸股市不存在波動溢出效應，且中國大陸股市對其他四個股市均不存在波動溢出效應。而中國香港股市與英國股市、中國香港股市與日本股市、美國股市與英國股市之間均存在雙向波動溢出效應，但美國股市對香港股市、美國股市對日本股市、英國股市對日本股市僅存在單向波動溢出效應。在動態(tài)相關(guān)性方面，各股市之間的相關(guān)關(guān)系具有時變特征，，且這種相關(guān)關(guān)系存在長記憶性，同時這種相關(guān)性在金融危機期間呈現(xiàn)出上升趨勢；此外，中國大陸股市與中國香港股市聯(lián)系最為緊密。在波動模型模擬效果方面，引入t分布的厚尾CCC-MSV模型與厚尾DCC-MSV模型要優(yōu)于未引入t分布的CCC-MSV模型與DCC-MSV模型。
[Abstract]:Since 1980s, economic globalization and financial integration have been continuously promoted in the global scope, which has greatly enhanced the interdependence of financial markets around the world. The volatility of a single financial market is affected not only by its own pre-fluctuation, but also by other financial market's pre-volatility. There is volatility spillover effect. However, most of the existing researches on volatility spillover effect in financial markets are based on GARCH model, compared with GARCH model. Stochastic volatility model is considered to be more suitable for the practical research in the financial field. Therefore, a Bayesian thick-tailed DCC-MSV model with bidirectional Granger test is constructed in this paper. It is used to study the relationship between volatility spillover effect and dynamic correlation between different financial markets. Firstly, the Bayesian analysis of the thick tail DCC-MSV model is carried out, and the MCMC sampling algorithm of the model parameter estimation is designed, which solves the difficult problem of estimating the parameters of the multivariable stochastic wave model. This paper introduces the method of parameter convergence diagnosis of stochastic volatility model, the comparison criterion of stochastic volatility model and the test method of volatility spillover effect significance. Finally, this paper selects Shanghai and Shenzhen 300 index and Hang Seng index. The Standard & Poor's 500 Index, the FTSE 100 Index and the Nikkei 225 Index are used as research objects to analyze the Chinese mainland stock market and the Chinese Hong Kong stock market after the split share structure reform in mainland China in 2005. The volatility spillover effect of American stock market, British stock market and Japanese stock market is related to the dynamic correlation. At the same time, the CCC-MSV model and the thick-tailed CCC-MSV model are analyzed by using DIC criterion. DCC-MSV model and thick tail DCC-MSV model were compared and analyzed. The results show that, in terms of volatility spillover effect, there is only one-way volatility spillover effect of Hong Kong stock market on mainland China stock market, the United States stock market. There is no volatility spillover effect on the mainland stock market in the UK and Japan stock market, and there is no volatility spillover effect on the other four stock markets in the Chinese mainland stock market, while the Hong Kong stock market and the UK stock market in China have no volatility spillover effects. There is a two-way volatility spillover effect between Chinese Hong Kong stock market and Japanese stock market, US stock market and British stock market, but the US stock market has a two-way volatility spillover effect on the Hong Kong stock market and the US stock market against the Japanese stock market. There is only one-way volatility spillover effect in the British stock market to the Japanese stock market. In terms of dynamic correlation, the correlation between the stock markets has time-varying characteristics, and this correlation relationship has a long memory. At the same time, the correlation showed an upward trend during the financial crisis; In addition, the mainland stock market and Hong Kong stock market are most closely linked. The thick tail CCC-MSV model and the thick tail DCC-MSV model with t distribution are better than the CCC-MSV model and DCC-MSV model without t distribution.
【學位授予單位】：湖南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F224;F832.51

【參考文獻】

相關(guān)期刊論文 前10條

1 田光;張瑞鋒;;基于Copula的股票市場波動溢出分析[J];財經(jīng)理論與實踐;2011年06期

2 楊飛虎;熊家財;;國際金融危機背景下國內(nèi)外股市波動溢出效應的實證研究[J];當代財經(jīng);2011年08期

3 余素紅,張世英;SV與GARCH模型對金融時間序列刻畫能力的比較研究[J];系統(tǒng)工程;2002年05期

4 張瑞鋒;張世英;;基于VS-MSV模型的金融市場波動溢出分析及實證研究[J];系統(tǒng)工程;2007年08期

5 曹廣喜;姚奕;;滬深股市動態(tài)溢出效應與動態(tài)相關(guān)性的實證研究——基于長記憶VAR-BEKK(DCC)-MVGARCH(1,1)模型[J];系統(tǒng)工程;2008年05期

6 張碧瓊;中國股票市場信息國際化:基于EGARCH模型的檢驗[J];國際金融研究;2005年05期

7 張瑞鋒;汪同三;;基于高頻數(shù)據(jù)的金融市場波動溢出分析[J];財經(jīng)理論與實踐;2013年01期

8 劉金全;崔暢;;中國滬深股市收益率和波動性的實證分析[J];經(jīng)濟學(季刊);2002年03期

9 洪永淼;成思危;劉艷輝;汪壽陽;;中國股市與世界其他股市之間的大風險溢出效應[J];經(jīng)濟學(季刊);2004年02期

10 趙留彥,王一鳴;A、B股之間的信息流動與波動溢出[J];金融研究;2003年10期

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