本文關(guān)鍵詞： 序列比對(duì) 符號(hào)時(shí)間序列分析 K-近鄰法 金融市場(chǎng)波動(dòng) 預(yù)測(cè)　出處：《天津大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著金融市場(chǎng)波動(dòng)的加劇及其在全球范圍內(nèi)的廣泛傳播，采用科學(xué)的方法對(duì)金融市場(chǎng)波動(dòng)進(jìn)行分析，對(duì)于預(yù)測(cè)金融市場(chǎng)波動(dòng)具有重要意義。其中金融計(jì)量學(xué)方法在金融市場(chǎng)波動(dòng)方面的研究已取得了豐碩的成果，但對(duì)于復(fù)雜的非線(xiàn)性經(jīng)濟(jì)系統(tǒng)來(lái)說(shuō)，單純以金融計(jì)量學(xué)的方法來(lái)研究金融波動(dòng)無(wú)法全方位地把握波動(dòng)的規(guī)律，需要利用新的方法從不同的角度來(lái)研究波動(dòng)問(wèn)題，作為金融計(jì)量學(xué)研究方法的補(bǔ)充，符號(hào)時(shí)間序列分析方法及序列比對(duì)方法正可以做到這一點(diǎn)。 本文引入生物信息學(xué)中的序列比對(duì)方法及非參數(shù)的符號(hào)時(shí)間序列分析方法，與已有的K-近鄰法相結(jié)合，提出一種新的金融波動(dòng)預(yù)測(cè)方法。利用符號(hào)化后的時(shí)間序列數(shù)據(jù)，將比對(duì)目標(biāo)序列與樣本序列進(jìn)行序列比對(duì)，通過(guò)動(dòng)態(tài)規(guī)劃算法回溯出高于匹配得分閾值的K條歷史子序列，以此作為K-近鄰法中的K個(gè)最近鄰，分別計(jì)算各自的權(quán)重，從而得到預(yù)測(cè)結(jié)果。以上證綜指、深證成指的高頻數(shù)據(jù)為樣本，對(duì)其價(jià)格波動(dòng)序列進(jìn)行實(shí)證分析；在成交價(jià)格波動(dòng)這個(gè)單一變量的基礎(chǔ)上，通過(guò)合適的符號(hào)化方法將兩維時(shí)間序列轉(zhuǎn)化為一維時(shí)間序列，從而擴(kuò)展到對(duì)成交價(jià)格波動(dòng)與交易時(shí)間間隔、成交價(jià)格波動(dòng)與成交量等兩個(gè)變量同時(shí)進(jìn)行預(yù)測(cè)，以個(gè)股萬(wàn)科的超高頻數(shù)據(jù)為樣本，進(jìn)行實(shí)證分析，驗(yàn)證了該方法的可行性和有效性。該方法可以捕獲時(shí)間序列的非線(xiàn)性特征，降低噪聲的敏感性，無(wú)需確定數(shù)據(jù)生成過(guò)程符合什么模型，，也不用做出數(shù)據(jù)是否平穩(wěn)等假設(shè)，不僅可以預(yù)測(cè)具體的波動(dòng)值，也可預(yù)測(cè)波動(dòng)所處的區(qū)間，適用范圍廣泛。 本文第一章闡述了對(duì)金融市場(chǎng)波動(dòng)進(jìn)行研究的背景、意義及國(guó)內(nèi)外研究現(xiàn)狀，并提出本文的創(chuàng)新點(diǎn)。第二章概述了兩個(gè)重要的基礎(chǔ)理論，符號(hào)時(shí)間序列方法及序列比對(duì)方法。第三章提出了基于序列比對(duì)方法的高頻金融波動(dòng)預(yù)測(cè)方法及其詳細(xì)步驟，并以上證綜指和深證成指采樣間隔為20分鐘的高頻數(shù)據(jù)為樣本進(jìn)行了實(shí)證分析，對(duì)波動(dòng)時(shí)間序列及波動(dòng)符號(hào)序列分別進(jìn)行了預(yù)測(cè)。第四章將單變量預(yù)測(cè)擴(kuò)展到雙變量預(yù)測(cè)，以個(gè)股萬(wàn)科2010年3月份的超高頻數(shù)據(jù)為樣本進(jìn)行了實(shí)證分析。第五章對(duì)全文的研究工作進(jìn)行了總結(jié)，指出序列比對(duì)方法在金融市場(chǎng)的研究中仍有很大的應(yīng)用、發(fā)展空間，并指明了下一步需要進(jìn)行的研究及改進(jìn)。
[Abstract]:With the aggravation of the financial market volatility and its wide spread in the global scope, the scientific method is used to analyze the financial market volatility. It is of great significance to predict the volatility of financial market. The research of financial metrology in the aspect of financial market volatility has made a lot of achievements, but for the complex nonlinear economic system. It is necessary to use new methods to study volatility from different angles as a supplement to the research methods of financial metrology, because it is impossible to grasp the law of volatility in all directions by using the method of financial metrology. Symbol time series analysis method and sequence alignment method can do this. In this paper, sequence alignment method and nonparametric symbolic time series analysis method in bioinformatics are introduced, which are combined with the existing K- nearest neighbor method. A new forecasting method of financial volatility is proposed, which compares the target sequence with the sample sequence by using the symbolic time series data. Through the dynamic programming algorithm to trace the K historical sub-sequences above the matching score threshold, as K nearest neighbors in the K-nearest neighbor method, calculate their respective weights, and then get the prediction results. The high frequency data of Shenzhen stock market index is taken as the sample, and the price fluctuation series is analyzed empirically. On the basis of the single variable of transaction price volatility, the two-dimension time series is transformed into one-dimensional time series by proper symbolization method, which extends to the interval between transaction price fluctuation and transaction time. Two variables, such as fluctuation of transaction price and volume of transaction, are predicted at the same time. The UHF data of individual stock Vanke are taken as the sample to carry on the empirical analysis. The method can capture the nonlinear characteristics of time series, reduce the sensitivity of noise, and do not need to determine the model of data generation process. It can not only predict the specific fluctuation value, but also predict the range in which the fluctuation is located, so it can be used in a wide range. The first chapter describes the background, significance and current research situation of financial market volatility, and puts forward the innovation of this paper. Chapter two summarizes two important basic theories. Symbolic time series method and sequence alignment method. In the third chapter, the prediction method of high frequency financial volatility based on sequence alignment method and its detailed steps are proposed. And based on the Shanghai Composite Index and Shenzhen Composite Index sampling interval of 20 minutes of high-frequency data as a sample for empirical analysis. In chapter 4th, univariate prediction is extended to bivariate prediction. Based on the UHF data of Vanke in March 2010, the empirical analysis is carried out. Chapter 5th summarizes the research work of the full text. It is pointed out that the method of sequence alignment still has a great application in the research of financial market, and the space for development is also pointed out, and the further research and improvement are pointed out.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：F830.91;F224

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