本文關鍵詞：金融復雜系統(tǒng)的特征研究及其交易策略構建　出處：《寧波大學》2014年碩士論文　論文類型：學位論文

  更多相關文章： 標度指數(shù) R/S分析 DFA分析 小波分析

【摘要】：復雜性是客觀事物不同層次的跨越，存在于客觀事物不同層次之間，金融市場是一個復雜的系統(tǒng)，在研究金融系統(tǒng)的復雜性時我們一般從下面幾個基本特征研究：證券價格序列的峰度和偏度、收益率的概率分布以及經驗分析等等。我國證券市場股票收益率具有典型的“尖峰厚尾”分布，所以在分析收益率的概率分布時，我們采用了列維平穩(wěn)分布，因為列維分布跟正態(tài)分布的區(qū)別是列維分布不僅具有對稱性而且還具有尖峰厚尾特征。經驗分析中我們采用對數(shù)回復率進行分析，分析上證指數(shù)對數(shù)回復率的自相自相關性。 基于對金融市場的長程記憶性研究，本文利用重標極差分析(R/S)和降趨脈動分析(DFA)這兩種方法分別研究了我國上證指數(shù)的長程記憶性。證實了中國證券市場上存在弱式長程記憶，以及存在多標度特征現(xiàn)象。本文根據(jù)中國證券市場上的弱式長程記憶性，以及它的物理意義，利用以往歷史數(shù)據(jù)，通過R程序構建了模型回測系統(tǒng)，檢驗是否可以根據(jù)弱式記憶性進行投資。通過對模型的收益率和大盤指數(shù)的收益率比較，發(fā)現(xiàn)通過弱式長程記憶可以幫助投資者做投資決策。 證券市場上的價格時間序列往往存在噪聲，噪聲嚴重的干擾了投資者對趨勢的判斷。而數(shù)學上的小波分析可以通過分解降噪，平滑信號，通過分解處理的信號，然后重新構造新的信號，新的信號往往平滑了很多。利用這個原理，本文對上證指數(shù)價格序列做了小波降噪分析，降噪處理后的時間序列的確平滑了很多。經過降噪處理后的時間序列，基本上可以認為是平穩(wěn)序列。本文選取了自回歸模型(AR)，，對經過處理后的平穩(wěn)時間序列進行預測，并且對預測結果進行了分析。
[Abstract]:Complexity is the leapfrogging of different levels of objective things and exists between different levels of objective things. Financial market is a complex system. In studying the complexity of financial system, we generally study the following basic characteristics: kurtosis and skewness of securities price series. The probability distribution and empirical analysis of the return rate. The stock return rate of China's stock market has a typical "peak thick tail" distribution, so in the analysis of the probability distribution of the return rate, we use Levi stationary distribution. Because the difference between Levi distribution and normal distribution is that the Levi distribution not only has symmetry but also has the characteristic of peak and thick tail. This paper analyzes the autocorrelation of logarithmic recovery rate of Shanghai Stock Exchange Index. Based on the long-term memory of financial markets. In this paper, we study the long range memory of Shanghai stock index by using the method of rescaled range analysis (R / S) and DFAA. It is proved that there is a weak long term memory in Chinese stock market. According to the weak long range memory in Chinese stock market and its physical meaning, this paper constructs a model retrieval system by using the historical data and R program. By comparing the return rate of the model with that of the large market index, it is found that weak long-term memory can help investors to make investment decisions. There is often noise in the time series of price in the stock market, which seriously interferes with the investors' judgment of the trend. The wavelet analysis in mathematics can smooth the signal by decomposing the noise. By decomposing the processed signal and then reconstructing the new signal, the new signal is often smooth. Using this principle, this paper does wavelet denoising analysis to the price sequence of Shanghai stock index. After noise reduction, the time series are smooth. After the noise reduction, the time series can be considered as stationary series. In this paper, the autoregressive model is selected. The stationary time series after processing are predicted, and the prediction results are analyzed.
【學位授予單位】：寧波大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F224;F830.9

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