【作者】 王文静；

【导师】 马军海；

【作者基本信息】 天津大学 ， 管理科学与工程， 2009， 博士

【摘要】 金融系统是现代经济的核心,其复杂性和波动性广泛存在于各国经济体制以及社会发展的各个阶段之中。我国于2001年底加入世界贸易组织,自此我国经济对外开放的步伐逐步扩大,而我国的金融业也于加入WTO五年后全面开放。这既为我们带来了发展机遇,同时也必然面临前所未有的挑战,中国的金融业在影响世界的同时,更多的则是不得不面对来自国际金融市场波动的影响。如何在金融市场国际化进程中抢占先机、采取积极有效的措施应对资本市场的波动成为我国发展金融市场的当务之急。为了能准确刻画金融时间序列的特征,就必须建立符合其特征的预测模型,而时间序列的记忆性则是建模的关键因素之一。对金融资产价格的记忆性进行研究是对其本质特征的研究,不但可以为监管层的政策制定和宏观调控提供可靠依据,还能够为机构和个人投资者提供实践指导。根据金融资产价格的特点进行投资决策,兼顾考虑市场的长、短期相关性的影响,才能抓住市场的本质,及时调整投资组合、规避风险。基于此,本文的主要创新点如下:1、同时运用R/S法、修正R/S法和V/S法对金融时间序列进行长记忆性分析。从时间和事件的角度对金融时间序列的长记忆性的影响进行了实证研究,结果表明不同的时间段和时间的选取会得到不同的检验结果。并对V/S分析法的短期敏感度进行了实证分析。2、以传统GM模型和ARMA模型为基础,利用IGM(1,1)模型来估计由ARFIMA(p,d,q)模型得到的模拟序列和真实值之间的偏差,提出了用来刻画长记忆金融时间序列的均值方程IGM-ARFIMA模型。通过对金融时序数据的预测研究表明改进后模型的预测效果优于原预测模型。3、以GM-GARCH模型为基础,针对长记忆性金融时间序列的波动率预测,利用IGM(1,1)模型对FIGARCH模型中的随机误差项加以修正,建立IGM-FIGARCH模型,即利用IGM(1,1)模型对FIGARCH模型中的随机误差项进行预测,然后将预测值加入到FIGARCH模型中,以修正不确定性因素产生的影响。实证研究表明IGM-FIGARCH模型优于GM-GARCH模型。4、对基本反馈型Elman网络进行改进,将其与相空间重构技术相结合,构建反馈型混沌神经网络,并对股票指数进行实证研究。结果表明多变量混沌神经网络的预测效果优于单变量混沌神经网络。更多还原

【Abstract】 Financial system is the core of modern economy. The complexity and volatility of the financial system widely exist in countries and each phase of society. China joined in World Trade Organization(WTO). From then on, China is increasingly opening its economy to foreign countries. Chinese financial market joined in WTO 5 years later. It brings us both the development opportunity and challenges that never faced before. While it is influencing the world, Chinese finance has to confront the volatility from the international financial market. How to take the advantageous position during the proceeding of finance internationalization and take the positive measures to reply to the volatility of the capital market have become the urgent affairs in our development of financial market.To show the characters of financial time series accurately, we must establish the proper models which accord with its characters. The memory character of time series is one of the key factors. Studying the memory of the prices of financial assets not only provides dependable foundation for authorities making policies and macro-economy regulations but also gives practical suggestions to the institutions and private investors. Only investing according to the characters of the financial assets considering both the long and short relativities can catch the essence of the market and adjust the portfolio in time to avoid the risk.Based on the above, the main works of the thesis are as follows.1、Use R/S, modified R/S and V/S analysis to research the long memory of financial time series. Study the impact factors of the long memory of financial time series from time and event point of view. The result indicates that varying time segments and special events can make the conclusions totally different. We also study the short sensitivity of V/S analysis.2、Based on the traditional GM model and ARMA model, use IGM model to estimate the error between the real value and the estimate value from the FIGARCH model. Propose the IGM-ARFIMA model to estimate the expectation of the long-term financial time series. Financial time series are forecasted with these models. The results indicate that modified model outperforms the original model.3、Based on the GM-GARCH model, according to the volatility forecast of long-term financial time series establish IGM-FIGARCH model using IGM model to correct error in FIGARCH model. That is to forecast the random error in FIGARCH model using IGM model and add the forecast value to the FIGARCH model to correct the influence of the uncertainty. The demonstrations indicate that IGM-FIGARCH model outperforms the GM-GARCH model.4、A modified Elman network is proposed. It has 2 feedback cells. Combine the modified Elman network and phase space reconstruction technique to establish a feedback chaos neural network. Stock price time series are forecasted with these methods. The experiments on the prediction of the specific financial series are carried out. The results indicate that multivariate chaos neural network outperforms the univariate one.更多还原