本文選題：波動(dòng)特征 + 聚類分析　； 參考：《浙江工商大學(xué)》2017年碩士論文

【摘要】：2014年下半年以來(lái),股市波動(dòng)的方差性堪比2008年的金融危機(jī),在如此劇烈的波動(dòng)情形下,對(duì)波動(dòng)特征更為全面的研究尤為重要,更能為投資者和監(jiān)管者應(yīng)對(duì)短期波動(dòng)提供有用的信息。本文圍繞股市波動(dòng)特征來(lái)進(jìn)行研究,將波動(dòng)特征分為微觀層面的聯(lián)動(dòng)性特征和宏觀層面的基于高頻分量的時(shí)變性進(jìn)行研究,旨在對(duì)波動(dòng)特征進(jìn)行更深入全面地研究。聯(lián)動(dòng)性的研究中,本文選用聚類分析方法,對(duì)上證50指數(shù)的內(nèi)部成分股結(jié)構(gòu)進(jìn)行分析,旨在對(duì)比分析不同波動(dòng)特征時(shí)期指數(shù)內(nèi)部聯(lián)動(dòng)性特征。首先對(duì)經(jīng)過(guò)篩選后的47個(gè)樣本成分股的日收盤價(jià)數(shù)據(jù)進(jìn)行處理,定義處理后的序列為收益—成交量序列,并對(duì)此序列根據(jù)四分位數(shù)進(jìn)行符號(hào)化得到新的符號(hào)化序列,根據(jù)這一新的符號(hào)化序列按照定義劃分的四個(gè)不同波動(dòng)特征階段分別進(jìn)行聚類。從四個(gè)階段的聚類群個(gè)數(shù)變化的角度和聚類得出的最小生成樹(shù)圖的分叉情況角度分析,行業(yè)的聯(lián)動(dòng)性隨著波動(dòng)的劇烈程度而逐漸減弱,并且在經(jīng)歷劇烈波動(dòng)后,無(wú)法立刻恢復(fù)到波動(dòng)前的結(jié)構(gòu)特征。在行業(yè)聯(lián)動(dòng)性中,金融業(yè)的行業(yè)聯(lián)動(dòng)性最為穩(wěn)定,反映出指數(shù)構(gòu)造時(shí)金融業(yè)占比較大的合理性。除此之外,也有其他的聚類特征,如地域性和審計(jì)機(jī)構(gòu)相似性�；诟哳l時(shí)變性的研究中,本文首先通過(guò)引用基于Huang(1998)提出的經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)基礎(chǔ)上改進(jìn)的集合經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD)的信號(hào)分解方法,通過(guò)對(duì)原始上證50信號(hào)序列加入白噪聲進(jìn)行分解,提取出高頻分量并進(jìn)行重構(gòu)后作為研究對(duì)象,由于提取出的高頻分量序列本身帶有噪音性質(zhì),因此在進(jìn)行一系列的檢驗(yàn)后,通過(guò)建立擬合得到的ARMA(3,2)-GARCH(1,2)模型對(duì)提取出的高頻分量進(jìn)行回歸分析。在回歸分析中,首先對(duì)不考慮成交量增量因素的高頻分量進(jìn)行分析,發(fā)現(xiàn)未考慮成交量之前,指數(shù)過(guò)去的波動(dòng)對(duì)于當(dāng)前波動(dòng)的影響為負(fù),即過(guò)去的波動(dòng)會(huì)減小當(dāng)前波動(dòng)的幅度;其次再對(duì)考慮了成交量增量因素的高頻分量進(jìn)行分析,發(fā)現(xiàn)指數(shù)過(guò)去的波動(dòng)對(duì)于當(dāng)前的波動(dòng)變成了正向的,即過(guò)去的波動(dòng)會(huì)加大當(dāng)前的波動(dòng)幅度。通過(guò)以上實(shí)證分析,并從行為金融學(xué)角度出發(fā),得出以下幾點(diǎn)結(jié)論:(1)行業(yè)的聯(lián)動(dòng)性在指數(shù)波動(dòng)中占主導(dǎo)地位,尤其是金融業(yè)的聯(lián)動(dòng)性最為穩(wěn)定,體現(xiàn)出指數(shù)構(gòu)造時(shí)選股的合理性;(2)聯(lián)動(dòng)性隨著波動(dòng)的劇烈程度而逐漸增強(qiáng),并且在極端波動(dòng)情形下會(huì)打破行業(yè)限制;(3)指數(shù)的波動(dòng)在短期內(nèi)會(huì)因?yàn)樾畔⑴丁⑼顿Y者情緒等因素對(duì)未來(lái)波動(dòng)形成不同方向的影響,不僅反映出了股市中“羊群效應(yīng)”等一些非理性現(xiàn)象的存在,也體現(xiàn)了“信息股市”的特點(diǎn);(4)無(wú)論是聯(lián)動(dòng)性的變化以及指數(shù)短期波動(dòng)特征,都體現(xiàn)股市波動(dòng)“記憶性”。最后,從投資者和監(jiān)管者的角度分別提出了建議。
[Abstract]:Since the second half of 2014, the variance of stock market volatility is comparable to that of the financial crisis in 2008. In such a volatile situation, a more comprehensive study of volatility characteristics is particularly important. More useful information for investors and regulators to deal with short-term volatility. This paper focuses on the volatility characteristics of the stock market. The volatility characteristics are divided into the micro level of the linkage feature and macro level based on the high frequency component of the time-varying study, aiming at a more in-depth and comprehensive study of volatility characteristics. In the study of linkage, this paper chooses the cluster analysis method to analyze the internal component structure of the Shanghai Stock Exchange 50 Index, aiming to compare and analyze the internal linkage characteristics of the index in different volatility periods. Firstly, the data of the daily closing price of 47 sample stocks are processed, and the processed sequence is defined as a profit-volume sequence, and a new symbolic sequence is obtained by symbolizing the sequence according to the quartile. According to this new symbolic sequence, four different fluctuation characteristic stages are divided according to the definition. From the point of view of the change of the number of clustering groups in four stages and the bifurcation of the minimum spanning tree graph obtained by clustering, the linkage of the industry is gradually weakened with the intensity of the fluctuation, and after experiencing the violent fluctuation, It is not possible to recover the structural characteristics immediately before the fluctuation. In the industry linkage, the financial industry is the most stable, which reflects the rationality of the financial industry when the index is constructed. In addition, there are other clustering features, such as regionalism and audit institution similarity. In the study of high frequency time-varying, the signal decomposition method of set empirical mode decomposition (EEMD) based on the empirical mode decomposition (EMD) proposed by Huang (1998) is introduced in this paper. By decomposing the original SSE 50 signal sequence with white noise, the high frequency component is extracted and reconstructed as the object of study. Because the extracted high frequency component sequence itself has the nature of noise, so after a series of tests, The regression analysis of the extracted high frequency components was carried out by establishing the fitting ARMA (3K2) -GARCH (1K2) model. In the regression analysis, the high frequency component which does not consider the increment factor of trading volume is first analyzed, and it is found that the influence of the past fluctuation of the index on the current fluctuation is negative, that is, the past fluctuation will reduce the amplitude of the current fluctuation. Secondly, by analyzing the high frequency component which takes into account the increment factor of trading volume, it is found that the past fluctuation of the index becomes positive to the current fluctuation, that is, the past fluctuation will increase the current fluctuation range. Through the above empirical analysis and from the point of view of behavioral finance, we draw the following conclusions: (1) the linkage of industry plays a dominant role in the index fluctuation, especially the linkage of financial industry is the most stable. It reflects the rationality of stock selection when the index is constructed; (2) the linkage increases gradually with the intensity of volatility and will break the industry restriction in extreme volatility; (3) the volatility of the index will be due to information disclosure in the short term. Investor sentiment and other factors affect the future fluctuations in different directions, which not only reflects the existence of some irrational phenomena such as "herd effect" in the stock market. It also reflects the characteristics of "information stock market". (4) both the linkage change and the short-term volatility of the index reflect the "memory" of the stock market volatility. Finally, from the perspective of investors and regulators, respectively, put forward suggestions.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 何凱;蘇h椒，

本文編號(hào)：2058679