基于集合經(jīng)驗?zāi)B(tài)分解的上證指數(shù)波動特性分析
發(fā)布時間：2018-11-08 20:37
【摘要】：我國股票市場經(jīng)過20多年的發(fā)展已經(jīng)成為我國市場經(jīng)濟(jì)的重要組成部分，股票市場的健康發(fā)展是我國經(jīng)濟(jì)穩(wěn)定發(fā)展的重要基礎(chǔ)。經(jīng)濟(jì)學(xué)界的眾多學(xué)者認(rèn)為，股票市場的價格波動與宏觀經(jīng)濟(jì)之間的關(guān)系密切。對兩個市場間的關(guān)系進(jìn)行深入地理論研究和實(shí)證檢驗，有利于資本市場的完善和健全，有助于股票市場的發(fā)展，對推進(jìn)我國社會主義經(jīng)濟(jì)建設(shè)和市場化改革起到至關(guān)重要的作用。因此，近年來經(jīng)濟(jì)金融學(xué)界越來越關(guān)注對股票市場價格波動與宏觀經(jīng)濟(jì)波動之間相互關(guān)系以及作用機(jī)理的研究。 美國工程院士黃鍔博士于1998年提出了一種新的信號分解方法——經(jīng)驗?zāi)B(tài)分解算法（Empirical mode decomposition，EMD），這種方法的本質(zhì)是通過數(shù)據(jù)的時間尺度特征來獲得本征波動模式，然后分解數(shù)據(jù)。依據(jù)數(shù)據(jù)自身的時間尺度特征來進(jìn)行信號分解，而無須預(yù)先設(shè)定任何基函數(shù)。與建立在先驗性假設(shè)的諧波基函數(shù)（或基頻）和小波基函數(shù)上的傅里葉分解與小波分解方法相比，它可以更準(zhǔn)確地反應(yīng)系統(tǒng)原始的物理特性，有更強(qiáng)的局部表現(xiàn)能力，在處理非線性、非平穩(wěn)信號或時間序列時往往更加有效。集合經(jīng)驗?zāi)B(tài)分解（Ensemble Empirical modedecomposition, EEMD）方法基于EMD算法，通過加入白噪聲的方式，解決了EMD方法中的模式混淆問題，，能夠更精確的分解數(shù)據(jù)序列。 本文首先利用信號分解中的EEMD方法，對上證指數(shù)的價格序列進(jìn)行分解，并通過平均周期、方差貢獻(xiàn)、Pearson相關(guān)系數(shù)三個指標(biāo)的角度對其進(jìn)行評價。進(jìn)一步地，將分解得到的IMFs劃分為高頻、低頻、趨勢三個分量，并對三個分量的經(jīng)濟(jì)學(xué)意義進(jìn)行解釋。本文認(rèn)為趨勢分量代表了上證指數(shù)的長期運(yùn)行趨勢，在本文的考察期內(nèi)上證指數(shù)的長期趨勢是上升的，但上升趨勢有所減緩；低頻分量反映了上證指數(shù)的中長期波動，其波動方向與上證指數(shù)基本一致，本文認(rèn)為低頻分量的波動受到宏觀經(jīng)濟(jì)的影響，并從直觀上發(fā)現(xiàn)低頻分量中的IMF6、IMF5分別與CPI和工業(yè)增加值的走勢十分接近；高頻分量的平均周期短，代表了上證指數(shù)的短期波動，其主要受到市場波動如投資者心理、短期事件政策刺激、外圍市場的波動溢出效應(yīng)等因素影響。 根據(jù)分解出的不同頻域分量的基本分析，本文對低頻和高頻分量有針對性的分別建立相應(yīng)模型進(jìn)行分析。針對低頻分量，本文將代表上證指數(shù)中長期波動的低頻分量單獨(dú)提取出來，與宏觀經(jīng)濟(jì)變量中的通貨膨脹率、工業(yè)增加值、貨幣供應(yīng)量M1三個變量建立VAR（3）模型，發(fā)現(xiàn)四者之間存在著長期均衡關(guān)系。通過協(xié)整檢驗和方差分解，得出結(jié)論認(rèn)為通貨膨脹與低頻分量負(fù)相關(guān)，即通貨膨脹會遏制上證指數(shù)中長期的向上波動趨勢，而工業(yè)增加值的增長能夠刺激上證指數(shù)的中長期上漲。同時，通貨膨脹與經(jīng)濟(jì)增長對上證指數(shù)中長期趨勢的影響程度相當(dāng)，而貨幣供應(yīng)量的增長雖然也有利于股市的中長期上漲，但其影響程度較低，表現(xiàn)為長期的“貨幣中性”。 針對高頻分量，本文主要分析上證指數(shù)與不同地區(qū)股票市場高頻波動間的相互影響。通過EEMD方法將股票指數(shù)的高頻分量提取出來，單獨(dú)研究他們之間的相關(guān)性，更能夠說明不同市場波動間的短期影響。本文對上證指數(shù)、香港恒生指數(shù)、美國道瓊斯工業(yè)指數(shù)的高頻分量分兩階段進(jìn)行Granger因果分析，發(fā)現(xiàn)第一階段（1997年至2006年）上證指數(shù)的高頻波動與恒生指數(shù)和道瓊斯工業(yè)指數(shù)相關(guān)性很弱，表現(xiàn)出一定的獨(dú)立性；而在第二階段（2007年至2014年）上證指數(shù)與道瓊斯工業(yè)指數(shù)高頻波動間存在著雙向的格蘭杰因果關(guān)系，相比于第一階段，在第二階段兩者的短期波動之間存在著顯著的相互影響。通過分析認(rèn)為其主要原因在于我國匯率改革進(jìn)程、資本賬戶的開放程度不斷加深，QFII與QDII的迅速發(fā)展是中美兩國資本市場間聯(lián)系程度加深的主要原因，另外由于美國在國際金融體系中的地位，使其次貸危機(jī)對資本市場的影響更容易傳播到其他市場；而第二階段港股的短期波動對上證指數(shù)有一定影響，相對于道瓊斯工業(yè)指數(shù)，上證指數(shù)對恒生指數(shù)的短期波動影響有限。 總之，本文將EEMD方法與傳統(tǒng)計量方法結(jié)合，通過EEMD分解方法將上證指數(shù)價格序列分為三個頻率層次的波動，分別研究不同頻率波動的影響因素，有助于準(zhǔn)確全面的分析不同因素對上證指數(shù)波動的影響，為投資者在股市中追求最大化收益提供建議，還能為決策層防范金融風(fēng)險，制定金融決策提供依據(jù)。
[Abstract]:The stock market has become an important part of China's market economy through the development of over 20 years, and the healthy development of the stock market is an important foundation of the development of our country's economy. Many scholars in the economic circle believe that the price fluctuation of the stock market is closely related to the macro-economy. The relationship between the two markets is in-depth theoretical research and empirical test, which is beneficial to the improvement and the sound of the capital market, which is conducive to the development of the stock market and plays an important role in the promotion of the socialist economic construction and the market-oriented reform of our country. Therefore, in recent years, more and more attention has been paid to the relationship between the price fluctuation of the stock market and the macro-economic fluctuation and the study of the mechanism of action. In 1998, a new method of signal decomposition _ empirical mode decomposition (EMD) was proposed by the American Academy of Engineering. data. The signal decomposition is performed according to the time scale characteristics of the data itself, without the need to set any base in advance. Compared with the wavelet decomposition method, the harmonic basis function (or fundamental frequency) and the wavelet-based function of a priori hypothesis can more accurately reflect the original physical characteristics of the system, have stronger local performance, a linear, non-stationary signal, or time sequence, often more The ensemble empirical mode decomposition (EEMD) method, based on the EMD algorithm, solves the model confusion problem in the EMD method by adding white noise, and can more accurately decompose the data. In this paper, the method of EEMD in signal decomposition is used to decompose the price sequence of the index, and the angle of the three indexes of Pearson's correlation coefficient is calculated by mean period, variance contribution and Pearson correlation coefficient. further, the decomposed IMFs are divided into three components of high frequency, low frequency and trend, and the economic meaning of the three components is This paper thinks that the trend component represents the long-term running trend of the Shanghai stock index, and the long-term trend of the index in the period of the investigation is up, but the rising trend is slow. The low-frequency component reflects the medium and long-term fluctuation of the Shanghai stock index, and its direction of fluctuation and the above-mentioned index refer to the above-mentioned index. The results show that the fluctuation of the low-frequency component is influenced by the macro-economy, and the IMF6 and IMF5 in the low-frequency component are found to be close to the trend of the CPI and the industrial added value, respectively. The average period of the high-frequency components is short, which represents the Shanghai Stock Index. The short-term fluctuation of the market is mainly affected by market volatility, such as investor psychology, short-term event policy stimulus, volatility spillover effect of peripheral market On the basis of the basic analysis of the decomposed different frequency-domain components, this paper sets up the corresponding establishment of the low-frequency and high-frequency components, respectively. Based on the low-frequency component, the low-frequency component, which represents the medium and long-term fluctuation of the index index, is extracted separately, and the VAR (3) model is set up with three variables of the inflation rate, the industrial added value and the money supply quantity M1 in the macro-economic variable. In the long-term equilibrium relationship, it is concluded that the inflation is negatively correlated with the low-frequency component, i.e., the inflation will contain the long-term upward fluctuation trend of the Shanghai stock index, while the growth of the industrial added value can stimulate the upper card. In the medium and long term of the index, the effect of inflation and economic growth on the long-term trend in the Shanghai Stock Index is comparable, and the growth of money supply is also beneficial to the mid-and long-term increase of the stock market, but its impact is low, and it appears as a long-term "water" currency y ". For high frequency components, this paper mainly analyzes the Shanghai stock index and the stock market in different regions The high-frequency component of the stock index is extracted by the EEMD method, the correlation between them is studied separately, In this paper, the high-frequency components of the Shanghai Stock Index, the Hong Kong Hang Seng Index and the Dow Jones Industrial Index were analyzed in two stages, and the high-frequency fluctuation and the Hang Seng index and the track of the first stage (1997-2006) were found. The Jones industrial index has a weak correlation and has a certain independence; in the second stage (2007-2014), there is a two-way Granger causality between the index and the high-frequency fluctuation of the Dow Jones Industrial Index, compared with the first phase, the short-term fluctuations in both the second and the second phase The main reason of the analysis is that China's exchange rate reform process and the opening degree of capital account are deepening, and the rapid development of QFII and QDII is the main reason for deepening the relationship between the two countries' capital markets. In the international financial system, the impact of the second loan crisis on the capital market is more likely to spread to other markets, while the short-term volatility of the second stage of the Hong Kong shares has a certain impact on the Shanghai Stock Index, with respect to the Dow Jones Industrial Index, the Shanghai Stock Index and the Hang Seng Index. In summary, the EEMD method is combined with the traditional measurement method, and the price sequence of the Shanghai stock index is divided into three frequency-level fluctuation by the EEMD decomposition method, and the influence factors of different frequency fluctuation are studied respectively, so that the accurate and comprehensive analysis is facilitated. The influence of different factors on the fluctuation of the Shanghai stock index is to provide advice to the investors in the stock market for maximizing the income, and also to guard against the gold at the decision-making level.
【學(xué)位授予單位】：浙江財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F832.51;F224
本文編號：2319623
[Abstract]:The stock market has become an important part of China's market economy through the development of over 20 years, and the healthy development of the stock market is an important foundation of the development of our country's economy. Many scholars in the economic circle believe that the price fluctuation of the stock market is closely related to the macro-economy. The relationship between the two markets is in-depth theoretical research and empirical test, which is beneficial to the improvement and the sound of the capital market, which is conducive to the development of the stock market and plays an important role in the promotion of the socialist economic construction and the market-oriented reform of our country. Therefore, in recent years, more and more attention has been paid to the relationship between the price fluctuation of the stock market and the macro-economic fluctuation and the study of the mechanism of action. In 1998, a new method of signal decomposition _ empirical mode decomposition (EMD) was proposed by the American Academy of Engineering. data. The signal decomposition is performed according to the time scale characteristics of the data itself, without the need to set any base in advance. Compared with the wavelet decomposition method, the harmonic basis function (or fundamental frequency) and the wavelet-based function of a priori hypothesis can more accurately reflect the original physical characteristics of the system, have stronger local performance, a linear, non-stationary signal, or time sequence, often more The ensemble empirical mode decomposition (EEMD) method, based on the EMD algorithm, solves the model confusion problem in the EMD method by adding white noise, and can more accurately decompose the data. In this paper, the method of EEMD in signal decomposition is used to decompose the price sequence of the index, and the angle of the three indexes of Pearson's correlation coefficient is calculated by mean period, variance contribution and Pearson correlation coefficient. further, the decomposed IMFs are divided into three components of high frequency, low frequency and trend, and the economic meaning of the three components is This paper thinks that the trend component represents the long-term running trend of the Shanghai stock index, and the long-term trend of the index in the period of the investigation is up, but the rising trend is slow. The low-frequency component reflects the medium and long-term fluctuation of the Shanghai stock index, and its direction of fluctuation and the above-mentioned index refer to the above-mentioned index. The results show that the fluctuation of the low-frequency component is influenced by the macro-economy, and the IMF6 and IMF5 in the low-frequency component are found to be close to the trend of the CPI and the industrial added value, respectively. The average period of the high-frequency components is short, which represents the Shanghai Stock Index. The short-term fluctuation of the market is mainly affected by market volatility, such as investor psychology, short-term event policy stimulus, volatility spillover effect of peripheral market On the basis of the basic analysis of the decomposed different frequency-domain components, this paper sets up the corresponding establishment of the low-frequency and high-frequency components, respectively. Based on the low-frequency component, the low-frequency component, which represents the medium and long-term fluctuation of the index index, is extracted separately, and the VAR (3) model is set up with three variables of the inflation rate, the industrial added value and the money supply quantity M1 in the macro-economic variable. In the long-term equilibrium relationship, it is concluded that the inflation is negatively correlated with the low-frequency component, i.e., the inflation will contain the long-term upward fluctuation trend of the Shanghai stock index, while the growth of the industrial added value can stimulate the upper card. In the medium and long term of the index, the effect of inflation and economic growth on the long-term trend in the Shanghai Stock Index is comparable, and the growth of money supply is also beneficial to the mid-and long-term increase of the stock market, but its impact is low, and it appears as a long-term "water" currency y ". For high frequency components, this paper mainly analyzes the Shanghai stock index and the stock market in different regions The high-frequency component of the stock index is extracted by the EEMD method, the correlation between them is studied separately, In this paper, the high-frequency components of the Shanghai Stock Index, the Hong Kong Hang Seng Index and the Dow Jones Industrial Index were analyzed in two stages, and the high-frequency fluctuation and the Hang Seng index and the track of the first stage (1997-2006) were found. The Jones industrial index has a weak correlation and has a certain independence; in the second stage (2007-2014), there is a two-way Granger causality between the index and the high-frequency fluctuation of the Dow Jones Industrial Index, compared with the first phase, the short-term fluctuations in both the second and the second phase The main reason of the analysis is that China's exchange rate reform process and the opening degree of capital account are deepening, and the rapid development of QFII and QDII is the main reason for deepening the relationship between the two countries' capital markets. In the international financial system, the impact of the second loan crisis on the capital market is more likely to spread to other markets, while the short-term volatility of the second stage of the Hong Kong shares has a certain impact on the Shanghai Stock Index, with respect to the Dow Jones Industrial Index, the Shanghai Stock Index and the Hang Seng Index. In summary, the EEMD method is combined with the traditional measurement method, and the price sequence of the Shanghai stock index is divided into three frequency-level fluctuation by the EEMD decomposition method, and the influence factors of different frequency fluctuation are studied respectively, so that the accurate and comprehensive analysis is facilitated. The influence of different factors on the fluctuation of the Shanghai stock index is to provide advice to the investors in the stock market for maximizing the income, and also to guard against the gold at the decision-making level.
【學(xué)位授予單位】：浙江財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F832.51;F224
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