本文選題：符號時間序列直方圖 + K-NN預(yù)測　； 參考：《天津大學(xué)》2012年碩士論文

【摘要】：高頻金融數(shù)據(jù)包含更多的市場信息，由于其在市場微觀結(jié)構(gòu)的實證研究方面的重要性而受到廣泛關(guān)注。對高頻金融波動的研究對股票估值、衍生產(chǎn)品定價、資產(chǎn)組合配置、風(fēng)險管理、貨幣政策的制定等至關(guān)重要，傳統(tǒng)分析方法針對具體的波動數(shù)據(jù)，，建立波動模型，本文則從不同的角度出發(fā)，分析與預(yù)測高頻金融波動的整體模式。 本文首先將符號時間序列分析方法與K-Nearest Neighbors(K-NN)算法相結(jié)合，提出了一種基于符號時間序列直方圖的高頻金融波動整體分布的預(yù)測方法。第一步將觀測所得的時間序列變換為符號時間序列，利用符號序列直方圖直觀表示符號序列的分布，引入符號直方圖時間序列的概念，采用K-NN算法得到下一個周期符號序列直方圖的預(yù)測。在K-NN算法中，針對符號序列直方圖的特點，提出以歐幾里得范數(shù)，χ2統(tǒng)計量和相對熵作為選擇鄰居時的符號直方圖序列相似度的度量方法，并利用系統(tǒng)自身的幾何特性確定符號直方圖序列的嵌入維數(shù)。其次，利用可以有效提取日內(nèi)信息的“已實現(xiàn)”波動來度量高頻金融時間序列的波動，首次使用具有魯棒性的排列熵方法分析“已實現(xiàn)”波動序列的順序模式、序列之間的廣義同步，利用全概率理論，在已知歷史“已實現(xiàn)”波動順序模式的情況下，預(yù)測下一個交易日的“已實現(xiàn)”波動處于不同水平的概率。 針對本文所提的方法，均以上證綜指或深證成指5分時的高頻數(shù)據(jù)檢驗了方法的可行性與有效性。結(jié)果表明直方圖時間序列的預(yù)測所得結(jié)果整體誤差均在可以接受的范圍內(nèi)，預(yù)測所得的分布與真實分布均值相同，但是方差較小；而基于排列熵方法分析時，發(fā)現(xiàn)這兩個指數(shù)的“已實現(xiàn)”波動序列之間基本不存在廣義同步，確定了它們的主要順序模式，并基于主要順序模式對“已實現(xiàn)”波動水平進(jìn)行預(yù)測，結(jié)果顯示主要順序模式的條件順序模式仍然占主要地位。
[Abstract]:High frequency financial data contains more information from the market, because of its importance in the empirical research of market microstructure and attracted widespread attention. The research on high frequency financial volatility of stock valuations, derivatives pricing, asset allocation, risk management, is the formulation of monetary policy, the traditional analysis method for wave specific data, establish fluctuation model, this article from a different perspective, the overall pattern analysis and prediction of high frequency financial volatility.
Firstly, the symbolic time series analysis method and K-Nearest Neighbors (K-NN) algorithm, presents a high frequency financial volatility of symbolic time series based on the histogram of the overall distribution forecasting method. The first step is to transform the time series of observed symbols for time series, using the visual representation of distribution symbol sequence histograms of symbol sequences, concept the introduction of symbolic time series prediction histogram, K-NN algorithm is used to get the next cycle of symbol sequence histograms. In K-NN algorithm, according to the characteristics of symbol sequence histograms, the Euclidean norm x 2, statistics and relative entropy as a measure method of symbol sequence similarity histogram when choosing neighbors, and using geometric characteristics the system itself to determine the embedding dimension histogram sequence of symbols. Secondly, use can effectively extract information on Realized Volatility To measure the high volatility of financial time series, the first use of permutation entropy method is robust analysis of realized volatility series order model, generalized synchronization between sequences, using the theory of probability, in the known history of realized volatility sequence model, forecast the next trading day "has been achieved" volatility in different levels of probability.
According to the method proposed in this paper, the high frequency data 5 points are the Shanghai Composite Index Shenzhengchengzhi or test the feasibility and validity of the method. The results show that the prediction results of the histogram of time series the overall error in the acceptable range, the income distribution prediction and the real distribution of mean the same, but the smaller variance analysis; permutation entropy based method, it was found that the two index of the realized volatility series does not exist between the basic generalized synchronization, and determine the main sequence pattern, and based on the main sequence model to predict the realized volatility level. The results showed that the main conditions of sequential mode sequential mode is still dominant.

【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F830.91

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

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