本文選題：多尺度分析　切入點：符號時間序列分析　出處：《天津大學》2012年碩士論文

【摘要】：金融波動性是金融市場的內(nèi)在屬性，與金融市場的功能與穩(wěn)定性密切相關，也是衡量一個市場效率和發(fā)展完善程度的指標。金融市場的波動性意味著市場中不確定性和風險，因此無論對于市場投資者還是市場監(jiān)管者而言，研究金融波動特性，全面、正確認識金融波動，把握金融波動特性，都有著重要的意義。金融市場本質上是一個非線性系統(tǒng)，影響因素眾多，變化復雜。分形、混沌分析和符號時間序列分析等非線性系統(tǒng)分析方法逐步引入金融市場的分析中，以期能夠更有效、更全面地揭示金融波動性的規(guī)律。眾多研究結果發(fā)現(xiàn)，金融市場波動存在多尺度現(xiàn)象，利用單一時間尺度分析金融波動得到的結果往往是片面的。因此本文從多尺度的角度出發(fā)，采用符號時間序列分析方法，對金融市場的波動特性展開研究，希望能夠全面且準確地認識金融波動的特性。 首先，文章論述了進行金融市場波動研究的背景及意義，總結了金融波動的研究成果，重點介紹了符號時間序列分析方法和小波多分辨分析的基本理論，為論文的展開提供理論基礎。然后將小波多分辨分析與符號時間序列方法結合，對將上證綜指和深證成指的“已實現(xiàn)”波動序列分解為不同尺度的細節(jié)分量，對原序列及不同的細節(jié)分量分別采用符號化分析，得出不同尺度上的符號序列直方圖，辨別確定不同時間尺度上的主要變化模式與異常變化模式，，為不同類型的投資者提供投資策略和風險管理的依據(jù)；提出符號序列秩次圖，直觀地研究不同序列之間以及同一序列在不同尺度上的相似性與差異性，體現(xiàn)符號時間序列分析的優(yōu)越性，計算簡便，減少了很多不必要的麻煩；采用歐幾里得范數(shù)、2統(tǒng)計量、相對熵以及秩次距離等符號時間序列的統(tǒng)計量，定量地分析不同序列之間不同尺度上的差異性，用具體的值描述差異性。 本文是國家自然科學基金項目“基于符號時間序列分析的金融波動研究”(項目編號：70971097)研究工作的一部分。
[Abstract]:Financial volatility is the intrinsic attribute of financial market, which is closely related to the function and stability of financial market, and is also an index to measure the market efficiency and the degree of development and perfection.The volatility of the financial market means the uncertainty and the risk in the market. Therefore, no matter for the market investors or the market regulators, we should study the characteristics of the financial volatility, understand the financial volatility correctly, and grasp the characteristics of the financial volatility.Are of great significance.Financial market is essentially a nonlinear system, which has many factors and complex changes.The nonlinear system analysis methods such as fractal, chaotic analysis and symbolic time series analysis are introduced into the analysis of financial market step by step, in order to reveal the law of financial volatility more effectively and comprehensively.Many studies have found that there are multi-scale phenomena in financial market volatility, and the results obtained by single time scale analysis are often one-sided.Therefore, from the point of view of multi-scale, this paper uses the symbolic time series analysis method to study the volatility characteristics of financial markets, hoping to fully and accurately understand the characteristics of financial volatility.First of all, the paper discusses the background and significance of financial market volatility research, summarizes the research results of financial volatility, focuses on the symbolic time series analysis method and wavelet multi-resolution analysis of the basic theory.To provide the theoretical basis for the development of the paper.Then the wavelet multi-resolution analysis is combined with the symbolic time series method to decompose the "realized" wave series of the Shanghai Composite Index and the Shenzhen Composite Index into the detail components of different scales.The symbol histogram of the original sequence and the different detail components is obtained by symbolic analysis on different scales, and the main change patterns and anomalous variation patterns on different time scales are identified by distinguishing the histogram of the original sequence and the different detail components, and the histogram of the symbol sequence on different scales is obtained.It provides the basis of investment strategy and risk management for different types of investors, and puts forward the rank graph of symbol sequence to study the similarity and difference between different sequences and the same sequence on different scales.The advantages of symbolic time series analysis are reflected, the calculation is simple and many unnecessary troubles are reduced, and the statistics of symbol time series, such as Euclidean norm, relative entropy and rank distance, are used.The differences between different scales are analyzed quantitatively and the differences are described with specific values.This paper is a part of the research work of the National Natural Science Foundation of China "Research on Financial volatility based on symbolic time Series Analysis" (item No.: 70971097).
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F224;F830.91

【引證文獻】

相關碩士學位論文 前1條

1 高正欣;基于符號序列分析的股市網(wǎng)絡結構及金融波動研究[D];天津大學;2014年

本文編號：1716320