本文關(guān)鍵詞：Agent-Based股指價(jià)格動(dòng)態(tài)預(yù)測(cè)模型構(gòu)造及分析　出處：《北京交通大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 謝爾賓斯基三角形 滲流 分形理論 峰度 復(fù)合多尺度熵 復(fù)雜性 可預(yù)測(cè)性 相關(guān)性

【摘要】：金融市場(chǎng)是一個(gè)復(fù)雜多變的動(dòng)力系統(tǒng),近年來(lái)的研究表明市場(chǎng)中的價(jià)格波動(dòng)呈現(xiàn)出大量不尋常的統(tǒng)計(jì)規(guī)律性(StylizedFacts),比如波動(dòng)聚集性、尖峰厚尾、長(zhǎng)程相關(guān)性等等,對(duì)股票市場(chǎng)波動(dòng)性的研究成為學(xué)術(shù)界的一個(gè)熱點(diǎn)。本文以分形理論為基礎(chǔ),創(chuàng)造性的在Sierpinski三角形分形地毯上構(gòu)建股指價(jià)格動(dòng)態(tài)預(yù)測(cè)模型,通過(guò)計(jì)算機(jī)編程實(shí)現(xiàn)仿真過(guò)程,探究模擬數(shù)據(jù)與真實(shí)數(shù)據(jù)在相應(yīng)統(tǒng)計(jì)規(guī)律性上的相似性,并對(duì)真實(shí)市場(chǎng)進(jìn)行短期預(yù)測(cè)。分形在金融市場(chǎng)中已經(jīng)得到了應(yīng)用廣泛,例如分形對(duì)金融數(shù)據(jù)表現(xiàn)出來(lái)的標(biāo)度不變規(guī)律性從理論高度上重新進(jìn)行了表述;多重分形將復(fù)雜體系分成許多奇異程度不同的區(qū)域研究,分層次了解其內(nèi)部結(jié)構(gòu);分形的自相似性與市場(chǎng)的波動(dòng)中整體和局部的相似性特點(diǎn)吻合等。本文借鑒Sierpinski地毯格點(diǎn)分形的研究成果,在Sierpinski三角形上構(gòu)建滲流模型模擬股指價(jià)格波動(dòng)。針對(duì)模擬的股指對(duì)數(shù)收益率時(shí)間序列,從整體分布的角度分析金融時(shí)間序列存在的尖峰厚尾分布特性,運(yùn)用復(fù)合多尺度熵的方法探究其復(fù)雜性,借助賴時(shí)本征相關(guān)性的方法分析股指之間的相關(guān)性;實(shí)證結(jié)果表明,股指價(jià)格動(dòng)態(tài)預(yù)測(cè)模型所模擬出來(lái)的數(shù)據(jù)在上述統(tǒng)計(jì)規(guī)律性上與實(shí)際數(shù)據(jù)存在一致性,說(shuō)明利用Sierpinski三角形分形創(chuàng)建的模型是合理的。本文最終根據(jù)歷史數(shù)據(jù)創(chuàng)建模型用于預(yù)測(cè)短期股指價(jià)格波動(dòng),并以隨機(jī)游走模型作為對(duì)比,對(duì)預(yù)測(cè)數(shù)據(jù)進(jìn)行誤差分析,直觀的說(shuō)明模型在市場(chǎng)預(yù)測(cè)中的價(jià)值。
[Abstract]:Financial market is a complex and changeable dynamic system. Recent studies show that price volatility in the market presents a large number of unusual statistical laws StylizedFacts. Such as volatility aggregation, peak thick tail, long-term correlation and so on, the research of stock market volatility has become a hot topic in academia. This paper is based on fractal theory. The dynamic prediction model of stock index price is constructed on the Sierpinski triangle fractal carpet creatively, and the simulation process is realized by computer programming. To explore the similarity between simulation data and real data in the corresponding statistical regularity, and to predict the real market in the short term. Fractal has been widely used in the financial market. For example, fractal represents the scale invariant law of financial data from the theoretical height; Multifractal divides the complex system into many regions with different degrees of singularity to understand its internal structure at different levels. The self-similarity of fractal coincides with the characteristics of global and local similarity in market volatility. This paper draws lessons from the research results of Sierpinski carpet lattice fractal. The seepage model is constructed on the Sierpinski triangle to simulate the price fluctuation of stock index. This paper analyzes the distribution characteristics of financial time series from the point of view of overall distribution, uses the method of complex multi-scale entropy to explore its complexity, and analyzes the correlation between stock indexes by means of the method of time-dependent correlation. The empirical results show that the data simulated by the dynamic forecasting model of stock index price are consistent with the actual data on the above statistical regularity. Finally, according to the historical data, the model is used to predict the price fluctuation of short-term stock index, and the random walk model is used as a comparison. Error analysis of forecasting data, intuitionistic explanation of the value of the model in market forecasting.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F830.9;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 牛紅麗;王軍;;基于選舉模型理論研究股市特性[J];北京交通大學(xué)學(xué)報(bào);2012年03期

2 王偉;馮志剛;;遞歸分形插值函數(shù)的計(jì)盒維數(shù)[J];安徽工業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年02期

3 季美峰;王軍;;應(yīng)用接觸過(guò)程構(gòu)造股票價(jià)格模型[J];北京交通大學(xué)學(xué)報(bào);2007年06期

4 韓書(shū)霞;戚大偉;于雷;;基于多重分形理論的原木CT腐朽圖像分析與處理[J];森林工程;2007年05期

5 邵明亭;王軍;;用選舉模型模擬股票收益過(guò)程的寬尾現(xiàn)象[J];北京交通大學(xué)學(xué)報(bào);2006年03期

6 苗玉樹(shù);;凈資產(chǎn)收益率指標(biāo)計(jì)算方法比較[J];財(cái)會(huì)研究;2006年06期

7 李亞靜,何躍,朱宏泉;中國(guó)股市收益率與波動(dòng)性長(zhǎng)記憶性的實(shí)證研究[J];系統(tǒng)工程理論與實(shí)踐;2003年01期

8 孫洪泉;分形幾何及其分形插值研究[J];河北工業(yè)大學(xué)學(xué)報(bào);2002年01期

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