本文關鍵詞： 長程相關性 多主體模型 尖峰胖尾 損失厭惡 金融物理　出處：《南京信息工程大學》2012年碩士論文　論文類型：學位論文

【摘要】：隨著經濟結構的演變,虛擬經濟的重要性日益顯現(xiàn)。1987年至今爆發(fā)了三次大的金融危機,這三次金融危機對全球實體經濟產生了深遠的影響。而經典金融理論難以解釋這些金融危機以及其他一些金融市場行為的產生原因。因為金融系統(tǒng)是典型的復雜系統(tǒng),所以本文基于復雜性理論,深入研究了金融市場波動的自依賴性,并構建了兩個多主體模型,研究了金融市場典型特征和投資者損失厭惡非理性行為形成的市場機理。本文主要的研究成果及創(chuàng)新性如下： (1)本文定義了平均波動性的變量,并分別研究了不同觀測尺度下金融市場指數(shù)波動特性。發(fā)現(xiàn)在較小的時間窗口下,金融時間序列波動前后之間存在顯著的線性關系。而隨著觀測窗口的變大,線性關系越來越弱,非線性關系越來越強。此外,本文研究了同一觀測尺度下道瓊斯工業(yè)平均指數(shù)不同時期金融波動性的前后相關性,發(fā)現(xiàn)了歷史上波動的相關結構會隨著樣本時間不同而變化。 (2)本文也對金融市場指數(shù)的GARCH模型進行了。真實數(shù)據和GARCH模型的分析結果在一定程度上是相似的,但進一步的定量研究表明兩者之間存在著顯著的差別。此外,研究結果發(fā)現(xiàn)GARCH模型低估了金融市場上的長程相關性。 (3)本文構建了一個多主體金融模型,將人與人之間的互動過程、機構投資者之間的非線性過程以及股票網絡拓撲結構的變化聯(lián)系起來,以研究金融市場上若干典型特征的產生機理。模型描述了羊群效應的產生過程以及股票市場拓撲結構的演變過程。模型分別在不同的初始條件和參數(shù)下進行了多次仿真,所產生的時間序列都具有波動聚集和尖峰胖尾特性,相關統(tǒng)計值與實際結果相符：中心標度值在0.579到0.747之間,對數(shù)收益累積分布在尾部明顯存在著冪率現(xiàn)象,冪指數(shù)約為3。此外,波動聚集程度與標準普爾500指數(shù)相仿,在納斯達克和香港恒生之間。仿真結果暗示市場拓撲結構的演變是對典型特征形成的重要因素。 (4)為了探索非理性行為和典型特征之間是否存在著共同的產生機制,本文建立了一個多主體模型研究了損失厭惡現(xiàn)象的形成機制。構建的模型成功再現(xiàn)了損失厭惡的形成過程。分析表明顯著的邊際效用遞減效應在損失厭惡的形成上起著重要的作用,而個體對杰出者的模仿行為也起著一定的作用。
[Abstract]:With the evolution of economic structure, the importance of virtual economy is becoming more and more obvious. Since 1987, three major financial crises have broken out. These three financial crises have had a profound impact on the global real economy. Classical financial theories are difficult to explain the causes of these financial crises and some other financial market behaviour, because the financial system is a typical complex system. Therefore, based on the complexity theory, this paper deeply studies the self-dependence of financial market volatility, and constructs two multi-agent models. This paper studies the typical characteristics of financial market and the market mechanism of investors' irrationality behavior of loss aversion. The main research results and innovations of this paper are as follows:. In this paper, the variables of average volatility are defined, and the volatility characteristics of financial market indices at different observation scales are studied. It is found that in a small time window, There is a significant linear relationship between the financial time series before and after the fluctuation. As the observation window becomes larger, the linear relationship becomes weaker and stronger, and the nonlinear relationship becomes stronger and stronger. In this paper, we study the correlation of financial volatility in different periods of the Dow Jones Industrial average at the same observation scale, and find that the correlation structure of historical volatility varies with the sample time. The GARCH model of financial market index is also studied in this paper. The results of real data and GARCH model are similar to some extent, but further quantitative research shows that there are significant differences between them. The results show that the GARCH model underestimates the long-range correlation in financial markets. 3) this paper constructs a multi-agent financial model, which links the interaction process between people, the nonlinear process among institutional investors and the change of topological structure of stock network. In order to study the generation mechanism of some typical characteristics in the financial market, the model describes the generation process of herding effect and the evolution of the topological structure of the stock market. The model is simulated several times under different initial conditions and parameters. The time series produced have the characteristics of fluctuation aggregation and peak fat tail, and the correlation statistical values are consistent with the actual results: the central scale values are between 0.579 and 0.747, and the logarithmic income accumulation distribution in the tail obviously has the phenomenon of power rate. The power index is about 3. In addition, the degree of volatility aggregation is similar to that of the S & P 500 index, between Nasdaq and Hang Seng in Hong Kong. The simulation results suggest that the evolution of market topology is an important factor in the formation of typical characteristics. (4) in order to explore whether there is a common mechanism between irrational behavior and typical characteristics, In this paper, a multi-agent model is established to study the formation mechanism of loss aversion. The established model successfully reproduces the formation process of loss aversion. The analysis shows that the significant marginal utility decline effect is in the formation of loss aversion. Has an important role to play, Individuals also play a certain role in imitating outstanding people.
【學位授予單位】：南京信息工程大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F224;F830.9

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本文編號：1534507