本文選題：歐債危機(jī)　切入點(diǎn)：波動(dòng)溢出效應(yīng)　出處：《江西財(cái)經(jīng)大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：本文針對(duì)歐債危機(jī)前后全球11個(gè)國(guó)家股票市場(chǎng)間波動(dòng)溢出效應(yīng)的關(guān)系變化進(jìn)行了深入研究,選取了英國(guó)、德國(guó)、法國(guó)、美國(guó)、中國(guó)、日本、意大利、西班牙、葡萄牙、希臘、愛(ài)爾蘭、歐洲大盤(pán)的股票指數(shù)為研究對(duì)象。首先對(duì)各國(guó)股市的原始收益率數(shù)據(jù)進(jìn)行BEKK-GARCH模型分析,但僅僅得到了各國(guó)溢出關(guān)系變化的粗略結(jié)論,不能看到不同時(shí)間尺度下的波動(dòng)溢出效應(yīng),如高頻數(shù)據(jù)間的波動(dòng)溢出效應(yīng)關(guān)系,長(zhǎng)期趨勢(shì)間的波動(dòng)溢出關(guān)系變化。為了研究歐債危機(jī)背景下,各國(guó)股票市場(chǎng)間不同時(shí)間尺度下的波動(dòng)溢出效應(yīng),本文對(duì)原始數(shù)據(jù)進(jìn)行了多分辨率分解,得到各國(guó)股票指數(shù)的高頻數(shù)據(jù)與長(zhǎng)期趨勢(shì)數(shù)據(jù),然后對(duì)高頻數(shù)據(jù)、長(zhǎng)期趨勢(shì)數(shù)據(jù)分別進(jìn)行BEKK-GARCH模型溢出效應(yīng)分析,從而得到不同時(shí)間尺度下各國(guó)股市間的波動(dòng)溢出關(guān)系變化情況,并分析各國(guó)股市不同時(shí)間尺度下波動(dòng)溢出效應(yīng)的特點(diǎn)。 目前常用的多分辨率分解方法是小波分解,但由于EMD方法具有比小波變換更強(qiáng)的局部表現(xiàn)能力,所以在處理非線(xiàn)性、非平穩(wěn)信號(hào)時(shí),是一種更為有效的方法。為了說(shuō)明EMD分解對(duì)數(shù)據(jù)具有更強(qiáng)的表現(xiàn)能力,本文對(duì)EMD分解后的高頻數(shù)據(jù)與小波分解后的高頻數(shù)據(jù)進(jìn)行對(duì)比,結(jié)果發(fā)現(xiàn)EMD分解后對(duì)數(shù)據(jù)的局部表現(xiàn)能力更強(qiáng),而小波分解后局部表現(xiàn)能力不強(qiáng)且樣本數(shù)量會(huì)減少一半,EMD分解后的長(zhǎng)期趨勢(shì)具有單調(diào)性,可以更清楚地刻畫(huà)數(shù)據(jù)序列的長(zhǎng)期走勢(shì),而小波分解后的低頻序列仍無(wú)明顯的規(guī)律。 將原始收益率數(shù)據(jù)的波動(dòng)溢出效應(yīng)分析簡(jiǎn)稱(chēng)為第一層次分析,將不同時(shí)間尺度下的波動(dòng)溢出效應(yīng)分析簡(jiǎn)稱(chēng)為第二層次分析。通過(guò)兩個(gè)層次的對(duì)比分析發(fā)現(xiàn),兩個(gè)層次的研究均得到了希臘是危機(jī)的主要輸出國(guó),葡萄牙是危機(jī)的主要傳遞國(guó),美國(guó)與其他國(guó)家間的溢出效應(yīng)最強(qiáng)。但僅通過(guò)第一層次的分析得出的結(jié)論較為粗糙,如通過(guò)第一層次得到的結(jié)論是“歐豬五國(guó)”與美國(guó)、中國(guó)、日本間的溢出效應(yīng)整體上減弱了,但通過(guò)第二層次的分析得到的結(jié)果是“歐豬五國(guó)”高頻序列與美國(guó)、中國(guó)、日本股市的高頻序列溢出效應(yīng)間呈現(xiàn)出不規(guī)律變化,而長(zhǎng)期趨勢(shì)間的溢出效應(yīng)則呈現(xiàn)規(guī)律變化：希臘、意大利、西班牙與美國(guó)、中國(guó)、日本股市間的聯(lián)動(dòng)性較強(qiáng),危機(jī)后仍呈現(xiàn)相互間的波動(dòng)溢出效應(yīng),愛(ài)爾蘭、葡萄牙向美國(guó)、中國(guó)、日本股市的溢出程度較弱,而更大程度地受美國(guó)、中國(guó)、日本股市向本國(guó)的波動(dòng)溢出效應(yīng)影響。 另外,通過(guò)第二層次的多尺度溢出效應(yīng)分析后發(fā)現(xiàn),有一部分結(jié)論通過(guò)對(duì)原始收益率數(shù)據(jù)進(jìn)行溢出效應(yīng)分析并未得到,而因EMD分解對(duì)數(shù)據(jù)具有更強(qiáng)的表現(xiàn)能力,得出的結(jié)論更為細(xì)致,如中國(guó)股市的“脆弱性”,美國(guó)與英國(guó)、中國(guó)與德國(guó)、日本與英國(guó)、英國(guó)與德國(guó)股市間存在著長(zhǎng)期趨勢(shì)間的相互溢出效應(yīng)。 從11個(gè)國(guó)家間的溢出效應(yīng)結(jié)果分析發(fā)現(xiàn),一個(gè)國(guó)家的經(jīng)濟(jì)越發(fā)達(dá),金融市場(chǎng)越穩(wěn)定,在危機(jī)發(fā)生后,該國(guó)股票市場(chǎng)則更多地向其他國(guó)家產(chǎn)生溢出效應(yīng),而不是受其他國(guó)家單向溢出效應(yīng)的影響。但由于我國(guó)仍處于發(fā)展中階段,金融市場(chǎng)較為“脆弱”,在危機(jī)發(fā)生后,向其他國(guó)家的溢出效應(yīng)由危機(jī)前的顯著變?yōu)椴伙@著,而受其他國(guó)家股市的溢出效應(yīng)在危機(jī)后增強(qiáng)了。最后,論文針對(duì)我國(guó)金融市場(chǎng)的“脆弱性”提出了金融市場(chǎng)發(fā)展的相關(guān)建議。
[Abstract]:The paper further study the change before and after the debt crisis in 11 countries in the world stock market volatility spillover effect were selected, the UK, Germany, France, the United States, Japan, Italy, China, Spain, Portugal, Greece, Ireland, the European stock market index as the research object. Firstly, the original income from stock market rate data were analyzed by BEKK-GARCH regression model, but only a rough conclusion change of every spillover relationship, can not see the volatility spillover effect under different time scales, such as the volatility spillover effect between the high frequency data, change the long-term trend between the volatility spillover relations. In order to study the European debt crisis, the volatility spillover effect between the stock markets of all countries under different time scales, the multi-resolution decomposition of the original data, get high frequency data for different stock index and long-term trend data, then Based on the analysis of BEKK-GARCH model spillover effect of high-frequency data and long-term trend data, we get the volatility spillover relationship of stock markets in different time scales, and analyze the characteristics of volatility spillover effects of different stock markets in different time scales.
The current commonly used multiresolution decomposition method is wavelet decomposition, but because the EMD method is better than the wavelet transform local performance, so in dealing with nonlinear, non-stationary signal, is a more effective method. In order to illustrate the decomposition of EMD has stronger the ability of data, this paper compares the high frequency data of EMD the high-frequency data and after wavelet decomposition, the results showed that local expression ability of data after the decomposition of EMD, and the wavelet decomposition of local performance ability and the sample number will be reduced by half, the long-term trend after the decomposition of EMD is monotone, the long-term trend can clearly describe the data sequence, and the low frequency sequence after wavelet decomposition is still no obvious regularity.
The analysis will be referred to as the first level analysis of the volatility spillover effect of data rate of the original income, will the volatility spillover effect under different time scales analysis referred to as the second level. Through the comparative analysis of two levels of analysis, study two levels were obtained in Greece is a major exporter of crisis, Portugal is the main transfer in crisis the United States and other countries, the spillover effect is strongest. But only through the first level of analysis the conclusion is rough, such as the first level of the conclusion is "PIIGS" with the United States, Japan Chinese, spillover effect between the overall weakening, but the results obtained through the analysis of the second level is "Ou pig" high frequency sequence and high frequency sequence Chinese, America, Japan's stock market spillover effect between showing no regularity, and the spillover effect between the long-term trend has changed: Greek law La, Italy, Spain and the United States, Chinese, Japan stock market linkage is stronger, after the crisis is still showing volatility spillover effects between Ireland and Portugal to the United States, Chinese, Japan's stock market spillover is relatively weak, and to a greater extent by the United States, China, Japan stock market volatility spillover effect to its influence.
In addition, the multi-scale analysis of spillover effect of the second level after the discovery, some conclusions through the analysis of spillover effects has not been on the original return data, due to the decomposition of EMD has stronger data performance, the conclusion is more detailed, such as Chinese stock market's vulnerability, the United States and the United Kingdom, Chinese with Germany, Japan and Britain, the British and German stock market there are spillover effects between the long-term trend.
From the results of the spillover effect between the 11 countries of the analysis found that the more developed the economy of a country, the financial market is more stable, after the crisis, the stock market is more spillover to other countries, and is not affected by other countries. But the spillover effects in China is still in development stage, the financial market is "fragile", after the crisis, the backward spillover effects in other countries by significant change before the crisis was not significant, but by the spillover effects of the stock market in other countries increased after the crisis. Finally, according to China's financial market "vulnerability puts forward relevant suggestions on the development of financial market.".

【學(xué)位授予單位】：江西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F224;F831.51

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本文編號(hào)：1622795