本文關(guān)鍵詞：Agent-Based股指價格動態(tài)預(yù)測模型構(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)測性 相關(guān)性

【摘要】：金融市場是一個復(fù)雜多變的動力系統(tǒng),近年來的研究表明市場中的價格波動呈現(xiàn)出大量不尋常的統(tǒng)計規(guī)律性(StylizedFacts),比如波動聚集性、尖峰厚尾、長程相關(guān)性等等,對股票市場波動性的研究成為學(xué)術(shù)界的一個熱點。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)建股指價格動態(tài)預(yù)測模型,通過計算機編程實現(xiàn)仿真過程,探究模擬數(shù)據(jù)與真實數(shù)據(jù)在相應(yīng)統(tǒng)計規(guī)律性上的相似性,并對真實市場進行短期預(yù)測。分形在金融市場中已經(jīng)得到了應(yīng)用廣泛,例如分形對金融數(shù)據(jù)表現(xiàn)出來的標度不變規(guī)律性從理論高度上重新進行了表述;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的波動中整體和局部的相似性特點吻合等。本文借鑒Sierpinski地毯格點分形的研究成果,在Sierpinski三角形上構(gòu)建滲流模型模擬股指價格波動。針對模擬的股指對數(shù)收益率時間序列,從整體分布的角度分析金融時間序列存在的尖峰厚尾分布特性,運用復(fù)合多尺度熵的方法探究其復(fù)雜性,借助賴時本征相關(guān)性的方法分析股指之間的相關(guān)性;實證結(jié)果表明,股指價格動態(tài)預(yù)測模型所模擬出來的數(shù)據(jù)在上述統(tǒng)計規(guī)律性上與實際數(shù)據(jù)存在一致性,說明利用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)測短期股指價格波動,并以隨機游走模型作為對比,對預(yù)測數(shù)據(jù)進行誤差分析,直觀的說明模型在市場預(yù)測中的價值。
[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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F830.9;F224

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