A Research On Stock Market’s Network Structure And Financial Volatility Based On Symbolic Seriesanalysis

With the development of national economy, the relationship between China’seconomic and the rest of the world’s ties more and more closely, at the same time, oureconomy is more and more influenced by the global financial market volatility. It is ofgreat importance for realizing and spreading financial risk to analyze and forecast thevolatility of financial market with scientific method. Abundant achievements havebeen made in the research of financial fluctuation with financial metrology method.However the economic system is a complicated nonlinear system, the rule of thefinancial volatility can’t be grasped in all aspects just with the financial metrologymethod. Thus, new research perspective and method need to be put forward. As asupplement of the financial metrology research method, symbolic time series analysismethod is tried to be introduced in the analysis of financial network and forecasting.Symbolic time series analysis method is introduced in this thesis, distance matrixis gotten based on the symbolic series’ coding series. On the basis of the above, theminimum spanning tree and hierarchical tree are gotten. And the analysis of stockmarket network structure is done according to that. Empirical analysis based on thestocks of Shanghai and Shenzhen300index is carried out. In the aspect of financialvolatility forecasting, new method is introduced based on symbolic time seriesanalysis and sequence alignment method. The best matching pattern is found bysymbolizing the time series，adopting the appropriate pattern length and sequenceallocating. The predictive value can be gotten based on the matching pattern. Andempirical analysis is made with high frequency data whose sampling interval is20minutes from Shanghai Stock Exchange Composite Index.The first chapter of this thesis states the background, significance and researchstatus of the relative research methods, combs the structure of this thesis and putsforward innovative points. The second chapter summarizes the important basic theoryused in the thesis, including symbolic time series method, network structure analysisand matching pattern forecasting. The third chapter introduces the stock market’snetwork structure analysis method based on symbolic time series, and empiricalanalysis based on the stocks of Shanghai and Shenzhen300index is carried out. Thefourth chapter introduces the financial volatility forecasting method based on pattern matching. Also sequence alignment methods used in the article is expounded in detailand empirical analysis is made with high frequency data whose sampling interval is20minutes from Shanghai Stock Exchange Composite Index. The fifth chaptersummarizes the full work in the text. It points out the direction for the next researchand improvement.