中文 English

图书详情

首页

英文文献

我的书架

当前位置: 首页 > 图书详情

中国金融风险预警研究

王小霞[著]

金融风险 预测 研究中国

2015-04-01

978-7-5161-5905-7

226

28

扫码阅读

  • 内容简介
  • 书籍目录
  • 作者简介
  • 参考文献
内容简介

2008年的金融危机百年不遇,在全球引起了“多米诺骨牌”效应,甚至出现了“国家倒闭”的极端案例。随着金融危机传染效应不断增大,对中国经济造成的负面影响已经从金融层面渗透到实体经济,影响程度之深已经远远超出了人们最初的预期。中国在很大程度上已融入到了经济全球化和金融一体化之中,诸多国际不稳定因素正通过多种传染渠道渗透到中国,与此同时,中国金融体系在运行中也存在一系列风险,在内外因素的影响下,导致中国金融业所面临的风险进一步加大。为保证中国金融业健康发展,防范金融危机对中国金融业造成的威胁,尽可能做到防患于未然,研究金融危机传染效应下中国金融风险预警机制的基础理论,设置科学完善的金融风险预警指标体系,选择适合中国国情的金融风险预警技术和方法等已成当务之急。
本书在经济全球化和中国金融自由化进程不断深化的现实背景下,从中国金融体系的内在脆弱性和运行中的诸多风险出发,以金融危机传染效应理论、金融风险预警理论为基础,同时运用向量标准化、AHP法、熵权法、因素分析法、乘数合成归一法、插值法、信号灯显示法、ADF检验、自回归模型、多元Logit模型等方法,从多学科、多工具、多角度深入研究了中国金融风险预警问题。本书的研究旨在为中国建立金融风险预警系统提供理论及实践依据,为设置金融风险预警指标体系和构建预警模型提供技术指导,为提高中国金融业运行的安全性提供新的思路和方法,具有理论价值和现实意义。
本书的创新点和主要发现如下:
第一,从金融危机传染效应和中国金融体系存在的内在脆弱性两个方面揭示了中国金融体系运行中的风险及进行风险预警的紧迫性。通过对金融危机由不同传染渠道传染到中国后引起中国金融体系出现系统性风险、实体经济部门和出口贸易受到影响、投资者对中国金融市场的投资行为发生改变及资源配置弱化等内容进行深入探析,发现在中国经济基本面发展不健全、金融体系本身具有内在脆弱性等内在风险的作用下,中国金融业面临的风险正在不断加大。因此,建立多角度、多层次的金融风险预警可以有效地防范中国金融风险,增强中国金融体系抵抗金融风险的能力,抑制金融风险转化为金融危机。
第二,开创性地构建了度量中国金融风险程度的指标——金融风险压力指数。首先对国际上流行的度量金融风险程度的指标——金融风险压力指数的构造进行了分析。然后,结合中国金融业运行的实际情况,去除掉对中国来讲因为没有完全市场化而意义有限的利率指标,加入对中国经济发展有重要影响的通货膨胀率指标,使得本书所构建的金融风险压力指数对中国金融风险程度的度量更具有现实意义,也更加符合中国当前的实际情况,突破了现有研究方法关于金融风险度量的局限性,实现了实践上的突破和方法上的创新。
第三,科学地设置了一套适合中国国情的金融风险预警指标体系。针对主观赋值法和客观赋值法各自存在的优点和不足,综合了AHP法(主观赋值法)设计指标具有专业性和熵权法(客观赋值法)设计指标具有客观性的优点,在确定中国金融风险预警指标体系的准则层和子准则层的基础上,分别利用AHP法和熵权法对预警指标体系中各指标权重进行确定并排序,然后利用乘数归一法对AHP法得到的预警指标权重进行熵权法调整,最终得到各预警指标的综合权重。在此基础上,利用插值法对中国历史经济数据进行标准化评分,并采用信号灯显示法对得到的预警指标体系进行验证分析,发现本书设置的中国金融风险预警指标体系能够有效地对中国1990年至2008年的金融风险程度进行事后验证,说明本书设置的中国金融风险预警指标体系科学性强,能够作为预警中国金融风险程度的指标体系。根据以上方法本书共设置了5大类28个风险预警指标。
第四,拓展了初始KLR模型,在单一预警指标的基础上建立了合成指标以提高KLR模型的预警能力,运用金融危机发生的条件概率对我国未来危机的发生进行预测,拓展后的KLR模型在平方概率得分、对数概率得分、全局平方偏差指标上比初始单一指标的KLR模型有着更为优秀的拟合优度和金融危机预警能力。从而建立我国金融风险预警系统,及时判断“警情”,发挥“报警器”作用,从多学科、多工具、多角度进行金融危机传染效应和金融风险预警研究,变风险的事后救助为事前预防,突破现有研究方法的局限。本书的研究表明2010年1月至2011年12月中国发生金融危机的概率很低。
第五,首次利用三元Logit模型更精确地对中国金融风险进行了预警。本书在二元Logit模型的基础上,通过引入新的虚拟因变量建立三元Logit预警模型。比较二元Logit模型和三元Logit模型的回归结果,预测中国金融危机的拟合优度以及检验三元Logit模型的稳健性,发现三元Logit模型在正确预测样本比率(86.67%>83.33%)、正确预测金融危机发生比率(36.11%>30.77%)以及所有发出危机信号的正确预测危机信号比率(81.25%>48%)等预测指标方面都要明显优于二元Logit模型,能够有效地将金融危机分解为危机发生时期、危机发生后经济恢复时期以及新的经济平稳时期,从而做到了可以最大限度地利用经济恢复时期的有用信息,提高了模型对中国金融风险预警的精确度。这种研究在国内是一个突破。
本书在研究撰写和出版过程中获得西安财经学院2014年科研资助和支持,在此深表感激!同时,本书在写作及数据整理过程中得到上海申银万国证券研究所宏观经济分析师徐有俊的帮助,在此一并感谢!

The 2008 financial crisis caused a "domino" effect around the world, and even an extreme case of "national failure". As the contagion effect of the financial crisis continues to increase, the negative impact on China's economy has penetrated from the financial level to the real economy, and the impact has far exceeded people's initial expectations. To a large extent, China has been integrated into economic globalization and financial integration, and many international unstable factors are penetrating into China through various contagion channels, at the same time, there are also a series of risks in the operation of China's financial system, which have led to further increased risks faced by China's financial industry under the influence of internal and external factors. In order to ensure the healthy development of China's financial industry, prevent the threat caused by the financial crisis to China's financial industry, and try to prevent problems before they occur, it has become a top priority to study the basic theory of China's financial risk early warning mechanism under the contagion effect of financial crisis, set up a scientific and perfect financial risk early warning index system, and select financial risk early warning technologies and methods suitable for China's national conditions. Under the background of economic globalization and the deepening process of China's financial liberalization, this book starts from the inherent fragility of China's financial system and many risks in operation, based on the theory of contagion effect of financial crisis and the theory of early warning of financial risks, and uses vector standardization, AHP method, entropy weight method, factor analysis method, multiplier synthesis normalization method, interpolation method, signal light display method, ADF test, autoregressive model, multivariate Logit model and other methods. This paper has in-depth research on the issue of early warning of China's financial risks from multiple angles. The research of this book aims to provide theoretical and practical basis for the establishment of financial risk early warning system in China, provide technical guidance for setting up financial risk early warning index system and building early warning model, and provide new ideas and methods for improving the safety of China's financial industry, which has theoretical value and practical significance. The innovations and main findings of this book are as follows: First, it reveals the risks in the operation of China's financial system and the urgency of risk early warning from two aspects: the contagion effect of the financial crisis and the inherent vulnerability of China's financial system. Through in-depth analysis of the systemic risks of China's financial system, the impact on the real economic sector and export trade, the changes in investors' investment behavior in China's financial market and the weakening of resource allocation after the financial crisis was transmitted to China by different channels of contagion, it is found that the risks faced by China's financial industry are increasing under the influence of inherent risks such as the imperfect development of China's economic fundamentals and the inherent fragility of the financial system itself. Therefore, the establishment of multi-angle and multi-level financial risk early warning can effectively prevent China's financial risks, enhance the ability of China's financial system to resist financial risks, and prevent financial risks from transforming into financial crises. Second, it pioneered the construction of the Financial Risk Stress Index, an indicator to measure the degree of financial risk in China. Firstly, the structure of the financial risk stress index, an internationally popular indicator to measure the degree of financial risk, is analyzed. Then, combined with the actual situation of the operation of China's financial industry, the interest rate index that has limited significance for China because it is not completely market-oriented is removed, and the inflation rate index that has an important impact on China's economic development is added, which makes the financial risk pressure index constructed in this book more realistic and more in line with China's current actual situation, breaks through the limitations of existing research methods on financial risk measurement, and achieves breakthroughs in practice and methodological innovation. Third, a set of financial risk early warning indicator system suitable for China's national conditions has been scientifically set up. In view of the advantages and disadvantages of the subjective assignment method and the objective assignment method, the professional design indicators of the AHP method (subjective assignment method) and the objectivity of the design indicators of the entropy weight method (objective assignment method) are synthesized, and on the basis of determining the criterion layer and sub-criterion layer of China's financial risk early warning index system, the AHP method and the entropy weight method are used to determine and rank the weights of each index in the early warning index system respectively, and then the entropy weight method is adjusted by the entropy weight method of the early warning index obtained by the AHP method by the multiplier normalization method. Finally, the comprehensive weight of each early warning indicator is obtained. On this basis, the interpolation method is used to standardize the scoring of China's historical economic data, and the signal light display method is used to verify and analyze the obtained early warning index system, and it is found that the China's financial risk early warning index system set up in this book can effectively verify the degree of China's financial risk from 1990 to 2008, indicating that the Chinese financial risk early warning index system set up in this book is highly scientific and can be used as an index system for early warning of China's financial risk. According to the above method, this book sets a total of 28 risk early warning indicators in 5 categories. Fourth, the initial KLR model is expanded, synthetic indicators are established on the basis of a single early warning index to improve the early warning ability of the KLR model, and the conditional probability of the occurrence of financial crisis is used to predict the occurrence of future crises in China, and the expanded KLR model has better goodness-of-fit and financial crisis early warning ability than the KLR model of the initial single index in terms of square probability score, logarithmic probability score and global square deviation indicators. Therefore, we can establish China's financial risk early warning system, timely judge the "alarm situation", give play to the role of "alarm", carry out research on the contagion effect of financial crisis and financial risk early warning from multidisciplinary, multi-tool and multi-angle perspectives, change the post-event rescue of risks to pre-prevention, and break through the limitations of existing research methods. The research in this book shows that the probability of a financial crisis in China between January 2010 and December 2011 is low. Fifth, for the first time, the ternary Logit model is used to more accurately warn China's financial risks. Based on the binary Logit model, this book establishes a ternary Logit early warning model by introducing a new dummy dependent variable. Comparing the regression results of the binary Logit model and the ternary Logit model, predicting the goodness-of-fit of China's financial crisis and testing the robustness of the ternary Logit model, it is found that the ternary Logit model correctly predicts the sample ratio (86.67% >83.33%), Correct prediction of the occurrence of financial crisis (36.11% >30.77%) and all correct prediction of crisis signals (81.25% >48%) and other predictive indicators are significantly better than the binary Logit model, which can effectively decompose the financial crisis into the crisis period, the post-crisis economic recovery period and the new economic stability period, so as to maximize the use of useful information in the economic recovery period. The accuracy of the model in early warning of China's financial risks was improved. This kind of research is a breakthrough in China. This book was awarded the 2014 scientific research funding and support of Xi'an University of Finance and Economics during the research writing and publication process, and I am deeply grateful! At the same time, in the process of writing and data collation, this book was helped by Xu Youjun, a macroeconomic analyst at Shanghai Shenyin Wanguo Securities Research Institute, and I would like to thank you for it!(AI翻译)

置顶