论文标题:可持续发展评价指标体系研究
Research on the Development of the Online Securities Trading in China
论文作者 吕潭华
论文导师 刘经华,论文学位 硕士,论文专业 经济思想史
论文单位 厦门大学,点击次数 132,论文页数 57页File Size9574k
2002-04-01论文网 http://www.lw23.com/lunwen_161124337/ 可持续发展;指标体系;实证分析
volatility,realized volatility,ARFIMAX (Auto Regression Fractional Integrated Moving Average with explanation)
目前国内外对可持续发展的研究和应用有很多方面。其中，对可持续发展的评价以及评价指标体系的建立是目前亟待深入研究的一个重要课题。如何评价可持续发展的状况和可持续发展的程度，可持续发展应该由哪些指标来表征以及如何通过这些指标来评估目前的发展是否具有可持续性，是一个关系到可持续发展理论体系和实际操作的重要问题。本文第一部分主要论析可持续发展评价的作用与意义。即可持续发展评价就是为了实现可持续发展的目标，运用科学的方法和手段来评价可持续发展运行的状况、实现的程度和效果，为指导可持续发展提供决策依据。 第二部分着重介绍和分析现有的可持续发展评价指标体系。自从可持续发展的概念被确认后，世界上很多研究机构为寻求它的评价指标做出了不懈的努力，他们提出了一些指标和指标体系，对可持续发展的研究和实践起到了一定的推动作用，但是这些指标得到公认或很好应用的却很少，到目前为止，仍然没有任何一个指标或指标体系能够赢得全世界的共同认可，或者能反映可持续发展的实质。现有的可持续发展评价指标体系各有其不同特点和适用范围，因而有必要把它们有机结合，互相补充。 为此，在本文的第三部分，笔者在对现有的各类指标体系的分析、研究和比较的基础之上，运用社会经济统计分析方法，从定性分析和定量分析两个角度继续探讨可持续发展评价指标体系的建立。其中，本文提出了一种新的分析方法——聚类分析方法，对经过定性分析得出的指标体系进行定量的检验，剔除可持续发展各子系统内部指标体系中存在着高度相关的指标，降低各子系统内部各指标之间的相关系数，使评价结果更具有客观性。 第四部分，以青岛市为例，针对青岛市的人口、资源、环境、经济与社会的各方面的状况分析，结合前文所述现有的可持续发展指标的理论，尤其是采用聚类分析方法进行定量研究，对可持续发展评价指标体系的建立过程进行实证分析，初步建立起青岛市可持续发展指标体系。笔者认为，可持续发展评价指标体系的建立是一项迫切需要深入研究的课题，有待学者们的共同努力。
Volatility is central to many applied issues in financial economics and financial engineering, ranging from asset allocation and derivative pricing to risk management. In Markowitz"s portfolio selection model (1959), volatility is one of the two dimensions. Inthe other backbone of modem financial economics ------ option pricing, volatility is themost important factor. And, in the dominating risk measure of applied finance ------ valueat risk, estimating volatility and covariance matrix is the first important step. Moreover, volatility is widely used in many other fields of financial economics, such as asset pricing and performance evaluation.For the last 20 years, volatility has been the hotspot of the financial economics. Since the first conditional volatility model by Engle (1982), thousands of papers concerning conditional heteroskedasticity have been published. Most recently, Anderson, Bollerslev, Diebold, Labys and other economists developed a new estimator of volatility, and, because of its high precision, we can regard it as observed volatility, or "realized volatility".Although some economists have developed and investigated the measuring and forecasting methods of realized volatility, there remain some deficiencies.First of all, asset price series does not follow normal diffusion process exactly. So the frequency of data used to estimate volatility should not be too high. Data of very high frequency would bring more errors due to microstructure friction. Torben G. Andersen and his coworker chose a data interval of 5 minute when studying the volatility of DAIJ 30 stocks. Later, they developed a method called "signature plot" to select proper data interval. However, the optimum data interval to balance the usual measuring error and the microstructure error would not be unique along all the sample periods. It seems to vary in different periods and different markets.Secondly, Andersen and his coworker developed FIVAR (fractional integrated vector auto regression) model to forecast exchange volatility. This model would not be proper when considering stock volatility because it does not take asymmetric effect into account.Thirdly, Ebens (1999) developed ARFIMAX model for forecasting stock volatility. He used the conditional sum-of-squares maximum likelihood method to estimate the parameters. This estimating method would cause large error when considering small datasample. Moreover, his ARFIMAX model deals with fractional differencing before asymmetric regression. As compared to the improved model developed in this thesis, it has disadvantages both in parameter estimation and explanatory adequacy.Lastly, previous models either consider only one time series, or consider more time series on the assumption that correlations do not change over time. This is unrealistic assumption.This paper develops a more accurate volatility measuring method, based on the previous ARFIMAX model, by systematically investigating the volatility of China stock indexes, covering high-frequency estimate, volatility characteristics, simulating and forecasting.First of all, we find that the microstructure bias of single stock is opposite to that of stock index. Using very high-frequency data, the realized volatility of single stock would be much higher, whereas that of stock index would be much lower. Based on the volatilities estimated with different data frequency, we develop a more accurate estimating method, which can effectively balance the microstructure error and usual estimate error.Secondly, we investigate some characteristics of the volatility for China stock indexes, such as the distribution of return, the distribution of volatility, the asymmetric effect of volatility, and the long memory effect of volatility.Thirdly, we develop a new mode based on ARFIMAX model to simulate and forecaste the volatility of China stock indexes and compare it with early models such as GARCH, EGARCH, FIGARCH and FIEGARCH. It has been shown that our model is better than the previous ones according to the data
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