基于“已实现”波动金融波动的符号时间序列分析

发布时间：2018-05-15 04:31

本文选题：符号时间序列分析 + “已实现”波动　；参考：《天津大学》2014年硕士论文

【摘要】：金融波动是金融市场风险的晴雨表。面对尚在发展中的国内金融市场，金融时间序列数值的起伏诠释了金融市场的内在属性稳定性。在现有的金融市场中，需要利用金融波动研究来分析金融风险，以期增强投资者的风险意识，引导投资者进行理性投资，促使金融市场有序健康地发展。因此，金融波动的相关预测与分析对我国金融市场具有重要的理论和实践意义。金融市场本身是一个时变、复杂的非线性系统，影响因素较多、结构复杂、变化多端。基于对上述情况的考虑，符号时间序列应用于金融波动分析能够更好地拟合复杂多变的金融市场，为国内外学者在金融市场领域的研究提供重要依据，对国内外金融市场的健康发展起着促进作用。因此，本文通过利用灰色马尔科夫模型预测金融波动来引导投资者行为，并通过聚类分析来归类风险，，帮助投资者实现风险规避、风险对冲。首先，本文介绍了金融波动的研究背景，提出金融波动研究的现实意义，总结了金融波动、“已实现”波动、符号时间序列分析的研究成果，从而指明论文的创新点、目的与研究意义。其次，本文提出了灰色马尔科夫模型、“已实现”波动、符号时间序列分析和聚类分析的具体方法理论，重点介绍了灰色马尔科夫模型和聚类分析的理论框架和工作步骤，为论文的相关研究提供理论依据。最后基于我国国内金融市场中上证指数和深证成指的“已实现”波动时间序列，利用灰色马尔科夫模型预测金融波动并利用聚类分析方法将现有金融市场的金融波动序列进行分类，从而能够使投资者全方位地了解我国金融市场，根据自己的风险偏好进行理性投资。本文仅为国家自然科学基金项目“基于符号时间序列分析的金融波动研究”(项目编号：70971097)研究工作的一部分。
[Abstract]:Financial fluctuation is a barometer of financial market risk. In the face of the developing domestic financial market, the fluctuation of financial time series value interprets the inherent property stability of financial market. In the present financial market, it is necessary to analyze the financial risk by using the financial fluctuation research, in order to enhance the risk consciousness of the investors, guide the investors to make rational investment, and promote the orderly and healthy development of the financial market. Therefore, the prediction and analysis of financial fluctuation is of great theoretical and practical significance to China's financial market. The financial market itself is a time-varying and complex nonlinear system with many influencing factors and complex structure. Based on the above situation, the application of symbolic time series to the analysis of financial volatility can better fit the complex and changeable financial markets, and provide an important basis for the research of domestic and foreign scholars in the field of financial markets. It plays an important role in promoting the healthy development of financial markets at home and abroad. Therefore, this paper uses grey Markov model to predict financial volatility to guide investor behavior, and cluster analysis to classify risk, help investors achieve risk aversion, risk hedging. First of all, this paper introduces the research background of financial volatility, puts forward the practical significance of financial volatility research, summarizes the research results of financial volatility, "realized" volatility, symbolic time series analysis, and points out the innovation of the paper. Purpose and significance of research. Secondly, this paper puts forward the theory of grey Markov model, "realized" fluctuation, symbol time series analysis and clustering analysis, and introduces the theoretical framework and working steps of grey Markov model and cluster analysis. To provide the theoretical basis for the related research. Finally, based on the "realized" volatility time series of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index in the domestic financial market, The grey Markov model is used to predict the financial fluctuation and the cluster analysis method is used to classify the financial fluctuation sequence of the existing financial market, so that the investors can understand the financial market in all aspects. Make rational investment according to your risk preference. This paper is only 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).
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F832.5;F224

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