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  本文關(guān)鍵詞：基于多元GARCH模型的流動性溢出效應(yīng)研究　出處：《浙江工商大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 金融危機 流動性 溢出效應(yīng) VAR 多元GARCH

【摘要】：隨著全球經(jīng)濟一體化進程加快,世界經(jīng)濟可謂風(fēng)起云涌變幻莫測。我國繼改革開放之后,走出了一個不平凡的三十年,九十年代之后開始走市場化道路,國內(nèi)生產(chǎn)總值更是以每年兩位數(shù)的發(fā)展速度高速增長,令世界各國嘆為觀止。然而在大跨步向前進的過程中,道路是坎坷的,1998年的亞洲經(jīng)濟危機、2008年美國次信貸危機所引爆的全球金融危機,都深刻的對中國這個新興的經(jīng)濟體給予了極大的考驗。與此同時,尤其是08年金融危機之后,流動性風(fēng)險在各國之間的傳遞效應(yīng)暴露在中國及世界各國面前,在毫無防備情況下,流動性的缺失使世界各國經(jīng)濟像多米諾骨牌一樣紛紛倒下。 在此背景基礎(chǔ)上,本文提出針對以中國、日本、美國和英國證券市場之間的流動性溢出效應(yīng)為研究對象。我們分別考慮了流動性度量指標(biāo)、樣本數(shù)據(jù)選取以及一定的模型選擇,從流動性水平和流動性風(fēng)險溢出效應(yīng)兩個角度,來度量他們之間的流動性生溢出效應(yīng)。流動性水平溢出效應(yīng)的刻畫,我們建立VAR向量自回歸模型,并在此基礎(chǔ)之上應(yīng)用格蘭杰因果關(guān)系檢驗、脈沖響應(yīng)分析、方差分解分析了變量之間的溢出效應(yīng)。流動性風(fēng)險溢出效應(yīng)的刻畫,我們從流動性指標(biāo)二階矩的估計上,最終選擇多元GARCH模型中具有代表性的BEKK. DCC-GARCH.GO-GARCH三個模型,在高維情況下進行了流動性風(fēng)險的實證分析,從橫向和縱向?qū)ξC前后以及各個模型之間得出的結(jié)論進行了分析比較,一方面應(yīng)用多元GARCH模型分析流動性風(fēng)險溢出效應(yīng),另一方面將多元GARCH的三類模型進行對比分析比較,以期進步推動存在維數(shù)災(zāi)難情況下多變量的研究。 經(jīng)實證研究之后得到以下結(jié)論,危機前中美證券市場流動性互為格蘭杰因果關(guān)系,中國證券市場為日本證券市場的單向格蘭杰英國關(guān)系；危機后,D工L_FS100、DIL_RJ225均為DIL_HS300的格蘭杰原因,而此時其它市場之間流動性并沒不存在顯著的因果關(guān)系。各市場對來自他們自身一個標(biāo)準(zhǔn)差新息響應(yīng)的時候,總是可以很快平復(fù)到零,對來自其它變量一個標(biāo)準(zhǔn)差新息的響應(yīng)的時候,他們都是以零為中心上下震蕩,并且在經(jīng)過十天之后回復(fù)到零。不管是危機前還是危機后,各國證券市場中流動性波動的貢獻因素主要為自己市場本身。波動風(fēng)險方面,危機后有了更高程度的波動持續(xù)性,且受各國在應(yīng)對危機上經(jīng)濟政策的一致性,各國之間的流動性風(fēng)險溢出效應(yīng)都表現(xiàn)為一定程度的正相關(guān),其中SP500與FS100正相關(guān)程度最高,原則上流動性的系統(tǒng)性風(fēng)險仍然存在。
[Abstract]:With the acceleration of the process of global economic integration, the world economy can be described as ups and downs of unpredictable. After the reform and opening up, China has been out of an extraordinary 30 years, after 90s began to take the road to marketization. Gross domestic product (GDP) is growing at a double-digit rate every year, which is amazing to the world. However, the road is bumpy in the process of big leap forward, the Asian economic crisis in 1998. In 2008, the global financial crisis triggered by the US sub-credit crisis gave a profound test to China, the emerging economy. At the same time, especially after the 2008 financial crisis. The transmission effect of liquidity risk between countries is exposed in front of China and other countries all over the world. Under the condition of defenseless, the lack of liquidity causes the economies of all countries to fall like dominoes. On the basis of this background, this paper proposes to study the liquidity spillover effects between China, Japan, the United States and the United Kingdom stock markets. We consider the liquidity metrics respectively. Sample data selection and certain model selection, from the liquidity level and liquidity risk spillover effect, to measure the liquidity spillover effect between them, the characterization of liquidity level spillover effect. Based on the VAR vector autoregressive model, we apply Granger causality test and impulse response analysis. Variance decomposition analyzes the spillover effect between variables. The characterization of liquidity risk spillover effect is based on the estimation of the second moment of liquidity index. In the end, we choose the representative BEKK. DCC-GARCH.GO-GARCH three models in the multivariate GARCH model, and carry on the empirical analysis of liquidity risk in the case of high dimension. The conclusions before and after the crisis and among the models are analyzed and compared horizontally and vertically. On the one hand, multiple GARCH models are used to analyze the liquidity risk spillover effects. On the other hand, three kinds of multivariate GARCH models are compared and analyzed in order to promote the multivariate research in the presence of dimensionality disaster. After the empirical study, the following conclusions are drawn: before the crisis, the liquidity of China and America stock market is Granger causality, and the Chinese stock market is the one-way Granger British relationship of Japanese securities market. After the crisis, LFS100 / Dil RJ225 is the Granger cause of DIL_HS300. At this point, there is not a significant causal relationship between other markets. When markets respond to a standard deviation innovation from themselves, they can always flatten to zero quickly. When responding to a standard deviation innovation from other variables, they oscillate around zero and return to zero after ten days, either before or after the crisis. The main contribution factor of liquidity volatility in the securities market of various countries is their own market itself. In terms of volatility risk, there is a higher degree of volatility after the crisis, and the consistency of economic policies in responding to the crisis. The spillover effect of liquidity risk between countries is positive correlation to some extent, in which SP500 and FS100 have the highest positive correlation. In principle, systemic risk of liquidity still exists.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F831.7

【參考文獻】

相關(guān)期刊論文 前10條

1 王金安;;中國股票市場流動性的實證研究[J];財會通訊;2011年24期

2 梁朝暉;上海股票市場流動性風(fēng)險與市場風(fēng)險的極值相關(guān)分析[J];中國地質(zhì)大學(xué)學(xué)報(社會科學(xué)版);2004年04期

3 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

4 陸賢偉;董大勇;紀(jì)春霞;;債市和股市波動非對稱性[J];系統(tǒng)工程;2009年09期

5 張蕊;王春峰;房振明;梁崴;;中國銀行間債券市場流動性風(fēng)險影響因素及其關(guān)聯(lián)性[J];系統(tǒng)工程;2010年03期

6 羅松;嚴(yán)敏;;我國股市與匯市間信息傳導(dǎo)的實證研究[J];系統(tǒng)工程;2010年12期

7 劉昊;;金融市場流動性波動溢出效應(yīng)的實證分析[J];南方金融;2011年01期

8 李成;王彬;黎克俊;;次貸危機前后中美利率聯(lián)動機制的實證研究[J];國際金融研究;2010年09期

9 劉瓊芳;張宗益;;基于Copula房地產(chǎn)與金融行業(yè)的股票相關(guān)性研究[J];管理工程學(xué)報;2011年01期

10 張宗成;王鄖;;股指期貨波動溢出效應(yīng)的實證研究——來自雙變量EC-EGARCH模型的證據(jù)[J];華中科技大學(xué)學(xué)報(社會科學(xué)版);2009年04期

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