本文關(guān)鍵詞：基于多重分形理論的我國行業(yè)股價波動性研究　出處：《南京信息工程大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 多重分形 多重分形譜 交叉相關(guān)性 行業(yè) 股價波動性

【摘要】：近年來,證券市場波動性已成為國內(nèi)外學(xué)術(shù)界研究的一個新熱點。眾所周知,我國股票市場作為一個新興市場,市場波動性較大,且不同行業(yè)的股價波動總是與市場的波動行為息、息、相關(guān)。傳統(tǒng)金融理論無法很好地描述實際金融市場復(fù)雜的波動特征,多重分形理論的誕生,從非線性的角度為金融市場的研究開辟了新的視野。本文從系統(tǒng)科學(xué)的角度出發(fā),基于多重分形理論對我國行業(yè)股價波動特征進(jìn)行研究,論文的主要工作和創(chuàng)新成果如下： (])借鑒信息熵概念對投資期限結(jié)構(gòu)的演變特征進(jìn)行了定量分析,揭示了投資期限結(jié)構(gòu)的演變對資產(chǎn)價格波動和市場穩(wěn)定的影響并有效驗證了分形市場假說。研究發(fā)現(xiàn)：當(dāng)市場上由不同投資期限的投資者組成時,投資期限結(jié)構(gòu)的信息熵和均衡度較大,投資期限結(jié)構(gòu)的一致性差,價格波動平穩(wěn),市場穩(wěn)定性強；而當(dāng)投資者的投資期限趨于一致時,投資期限結(jié)構(gòu)的信息熵和均衡度變小,價格波動劇烈,市場穩(wěn)定性變?nèi)酢?(2)以2007年10月17日為轉(zhuǎn)折點,利用多重分形去趨勢波動分析法(MF-DFA)比較分析了危機前后滬深300十大行業(yè)指數(shù)的奇異性特征。結(jié)果表明：危機前后各行業(yè)指數(shù)都具有多重分形特征；與其它行業(yè)相比,危機前期電信、工業(yè)、可選和信息行業(yè)的譜寬度更寬,波動更劇烈,危機后期金融、能源行業(yè)的譜寬度更寬,波動更劇烈；與危機前期相比,能源和金融行業(yè)危機后期的譜寬度變寬,波動變劇烈,而其它行業(yè)危機后期的譜寬度變窄,波動變平穩(wěn)；就危機前后譜寬度的變化來說,能源、工業(yè)、可選、信息、消費和電信行業(yè)比其它行業(yè)變化幅度大,受危機的影響更顯著。 (3)以2005年4月8日至2009年12月31日道瓊斯和滬深300十大行業(yè)指數(shù)的日收益率為樣本,在檢驗美國股市和我國行業(yè)長期相關(guān)性的基礎(chǔ)上,利用多重分形去趨勢相關(guān)分析法(MF-DXA)并結(jié)合滑動窗口技術(shù),比較分析了不同經(jīng)濟時期道瓊斯和滬深300十大行業(yè)收益率序列交叉相關(guān)的多重分形特征,揭示了美國股市對我國行業(yè)股價波動的影響。實證結(jié)果表明：危機前期,道瓊斯和電信、工業(yè)、消費和可選行業(yè)交叉相關(guān)的譜寬度較寬,多重分形特征較強,相關(guān)關(guān)系較復(fù)雜；危機初期,道瓊斯和消費、可選、金融和信息行業(yè)交叉相關(guān)的譜寬度較寬,多重分形特征較強,相關(guān)關(guān)系較復(fù)雜；危機后期,道瓊斯和金融、醫(yī)藥、消費、能源行業(yè)交叉相關(guān)的譜寬度較寬,多重分形特征較強,相關(guān)關(guān)系較復(fù)雜。與危機初期相比,危機前后期道瓊斯和除消費以外的各大行業(yè)交叉相關(guān)的譜寬度更寬,多重分形特征更強,相關(guān)關(guān)系更復(fù)雜,我國行業(yè)股價波動受美國股市影響更顯著。 以上結(jié)論不僅有利于投資者估計行業(yè)風(fēng)險、合理進(jìn)行投資,還有利于各行業(yè)企業(yè)自身的發(fā)展,同時也有利于市場監(jiān)管者對市場進(jìn)行有效調(diào)控,保證股市和經(jīng)濟的健康發(fā)展。
[Abstract]:In recent years, the volatility of the securities market has become a new focus of academic research at home and abroad. As we all know, the stock market of our country is a new emerging market, the market volatility is large. And the volatility of stock price in different industries is always related to the volatility behavior of the market interest, interest, correlation. Traditional financial theory can not describe the complex volatility characteristics of the actual financial market, the birth of multifractal theory. From the point of view of nonlinearity, it opens up a new field of vision for the study of financial market. From the point of view of system science, this paper studies the characteristics of stock price volatility in China industry based on multifractal theory. The main work and innovative results of the thesis are as follows: (]) using the concept of information entropy for reference, the paper makes a quantitative analysis of the evolution characteristics of the term structure of investment. It reveals the influence of the evolution of investment term structure on asset price fluctuation and market stability and effectively verifies the fractal market hypothesis. The information entropy and equilibrium of the investment term structure are large, the consistency of the investment term structure is poor, the price fluctuation is stable, and the market stability is strong; When the investment term of the investor tends to be consistent, the information entropy and equilibrium degree of the investment term structure become smaller, the price fluctuates violently, and the market stability becomes weaker. Take October 17th 2007 as the turning point. Using multifractal detrend fluctuation analysis method. The singularity characteristics of Shanghai and Shenzhen 300 ten industry indexes before and after the crisis are compared and analyzed. The results show that: before and after the crisis, every industry index has multifractal characteristics; Compared with other industries, the spectrum width and fluctuation of telecommunications, industry, optional and information industry in pre-crisis period are wider and more intense, and the spectrum width of finance and energy industry is wider and more volatile in post-crisis finance industry. Compared with the early period of the crisis, the energy and financial industries' spectrum width became wider and more volatile in the latter stage of the crisis, while the latter period of the other industries' crisis became narrower and more stable. In terms of changes in spectrum width before and after the crisis, the energy, industry, options, information, consumer and telecommunications industries have changed more rapidly than other industries, and are more affected by the crisis. From April 8th 2005 to December 31st 2009, the daily yields of the top 10 Dow Jones and Shanghai and Shenzhen 300 industry indices were taken as samples. On the basis of testing the long-term correlation between American stock market and Chinese industry, the multifractal detrend correlation analysis method (MF-DXA) and sliding window technique are used. The multifractal characteristics of cross-correlation between Dow Jones and Shanghai and Shenzhen 300 industry yield series in different economic periods are analyzed. The empirical results show that in the pre-crisis period, the cross-correlation spectrum of Dow Jones and telecom, industry, consumption and optional industries is wider and multi-fractal features are stronger. The correlation relation is complex; At the beginning of the crisis, Dow Jones and consumption, optional, financial and information industry cross-correlation spectrum width is wider, multi-fractal features are stronger, the correlation relationship is more complex; After the crisis, Dow Jones and finance, medicine, consumption, energy industry cross-correlation spectrum width is wider, multi-fractal features are stronger, the correlation is more complex, compared with the initial crisis. Before and after the crisis, Dow Jones and the major industries other than consumption have wider spectrum width, stronger multifractal features and more complex correlation, and the volatility of Chinese industry stock price is more significantly affected by the U.S. stock market. The above conclusions are not only helpful for investors to estimate the industry risk, reasonable investment, but also conducive to the development of enterprises in various industries, but also conducive to the effective regulation and control of the market by market regulators. To ensure the healthy development of the stock market and the economy.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.51;F224

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本文編號：1428758