An Empirical Study On China's Banking Fragility Measurement

More and more countries are suffered from banking crisis, which makes regulators and scholars have to study banking fragility. It is the key that how to measure banking fragility. The relevant issues are how to evaluate the banking fragility in a country and how to realize the factors which possibly lead to banking crises. The solution to the problems is to improve the level of external supervision and internal managements. The measurements of banking fragility at home and abroad are different. There are event-based method, camels frame, KLR method, etc. These methods have their advantages, disadvantages and applicability, respectively. The author made comments on these methods in this paper, and argue that a good model on the measurement of banking fragility must have three characteristics. The first is the independent variables of the key factors which lead to banking crises or banking failure. The second is that the model should be simple, accurate and sensitive advantage. The third is that the model should have forecast function, not only afterwards measurement. On the basis, the author constructs a banking fragility index (BFI). Then use the BFI model to measure the degree of banking fragility of nine banks in 1999 to 2007 in China. At last, the author analyzed the key factors which lead to bank crises or bank failure by Logistic regression model. The paper comprises the following 4 parts:The preface part introduces the background, the significance of the study and gives a brief introduction to the related studies of scholars home and abroad. The readers can learn about the study on measurement of banking fragility in China is primary. There are lots of issues should be solved. The second chapter discusses the connotation of banking fragility. Then it gives the brief introduction of basic theories of banking fragility, which is the foreshadowing in theory for analyzing next. The third chapter introduces measurements of banking fragility at home and abroad. The author makes comments on these methods. Based on these analyses, the author constructs banking fragility index. Finally, the measurements to the banks of china fragility have been done by banking fragility index. In chapter 4, the author firstly introduces Logistic model, then makes a regression analyses based on 9 banks.Learning from other scholars'research results, the author carries on innovative research here.The first, the author makes deep discrimination on the concept of banking fragility. Concept is the starting point of our research. Scholars have not reached a consensus of the concept of banking fragility at present. They explain the concept in different respects such as formation mechanism, inherent characteristic, financial risk. After discrimination, the author proposes two concepts of the narrow sense for banking fragility and the broad sense for banking fragility and delineated a flowchart which shows the relationship among the narrow sense of banking fragility, unsystematic risk of bank, systematic risk of banking, systemic risk of banking, shocks and banking crisis. The flowchart also shows the formation mechanism of banking crisis.The second, the author comments on the measurements of banking fragility such as event -based method, camels frame, KLR method, etc, and proposes the advantage or disadvantage for each method.The third, the author constructed banking fragility index (BFI). The BFI is based on chi-square distribution theory. By comparing practical level with historical average level of the key factors to banking fragility, you can find which factor is deviated far from historical average level. Furthermore, by this way, you can find which bank is fragility.The fourth, the measurements of banking fragility in this paper is an approach of uphill. At present, most of scholars in our country use total amount to measure bank- ing fragility. Obviously, the method of total amount may cover individual character, which may provide misleading information. Moreover, banking crisis often originates from single bank failure. The author uses the method uphill to measure banking frag- ility and Logistic regression.Of course, this paper has a few shortcomings. The main as below:The first, the measurements is just a static method. We can estimate the probability of each bank. Next, the probabilities should be multiplied by the weight of assets in banking industry and be summed. Finally, we can obtain the probability of banking crisis. In this process, I ignore the systemic risks of banking. Therefore, it is a static method.The second, the sample period (from 1999 to 2007) is short-run, can not explain effectively the significant of macro variables. While, five levels classification of non-performing loans is carried out in banking industry in our country since 1999. In order to keep comparability of data, we have to select this sample.