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2022 | Buch

Financial Statements-Based Bank Risk Aggregation

verfasst von: Jianping Li, Assist. Prof. Lu Wei, Xiaoqian Zhu

Verlag: Springer Singapore

Buchreihe : Innovation in Risk Analysis

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This book proposes a bank risk aggregation framework based on financial statements. Specifically, bank risk aggregation is of great importance to maintain stable operation of banking industry and prevent financial crisis. A major obstacle to bank risk management is the problem of data shortage, which makes many quantitative risk aggregation approaches typically fail. Recently, to overcome the problem of inaccurate total risk results caused by the shortage of risk data, some researchers have proposed a series of financial statements-based bank risk aggregation approaches. However, the existing studies have drawbacks of low frequency and time lag of financial statements data and usually ignore off-balance sheet business risk in bank risk aggregation. Thus, by reviewing the research progress in bank risk aggregation based on financial statements and improving the drawbacks of existing methods, this book proposes a bank risk aggregation framework based on financial statements. It makes full use of information recorded in financial statements, including income statement, on- and off-balance sheet assets, and textual risk disclosures, which solves the problem of data shortage in bank risk aggregation to some extent and improves the reliability and rationality of bank risk aggregation results. This book not only improves the theoretical studies of bank risk aggregation, but also provides an important support for the capital allocation of the banking industry in practice. Thus, this book has theoretical and practical importance for bank managers and researchers of bank risk management.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Basic Concepts of Bank Risk Aggregation
Abstract
Banks are one of the most important financial institutions in the economic system. The stability of banks is very important for the stability of the whole financial system and the smooth operation of the economy. In the process of operation, banks will inevitably face various types of risks, such as credit risk, market risk and operational risk. Therefore, risk management is necessary to maintain the stability. Regulatory agencies such as the Basel Committee on Banking Supervision have realized the importance of risk management and have formulated the Basel Accord. Bank risk aggregation is the process of calculating the bank's overall risk after considering the correlation between different risks faced by the bank, which is the core issue of bank risk management. Thus, this chapter introduces the Basel Accords and the concepts of variety types of bank risks and bank risk aggregation.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 2. Research Review of Bank Risk Aggregation
Abstract
We divide the process of bank risk aggregation into four key aspects, and systematically review the research on bank risk aggregation under correlation from three levels: the correlation of bank risk aggregation, the typical characteristics of correlation, the method of bank risk aggregation and the risk data in bank risk aggregation. The correlation relationships of bank risk aggregation are complicated. There are correlations among banks, among different types of risks within banks, and between levels and elements within risks. After determining the correlation relationship between different bank risks, the characteristics of the correlation structure between risks need to be consider. There are many complex characteristics in the correlation of bank risk, such as nonlinearity, tail correlation, structural asymmetry and so on. Finally, the risk aggregation method is selected to integrate the bank risk. Different risk aggregation methods have different abilities to describe various complex characteristics of risk correlation. Only by fully capturing these typical characteristics, can the risk aggregation method accurately describe the correlation between risks and obtain accurate results of bank risk aggregation.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 3. Financial Statements-Based Bank Risk Aggregation Framework
Abstract
Due to the lack of data, many risk integration methods do not play a good role in managing and preventing bank risks. As the financial statements have the characteristics of easy access, standardized disclosure and comprehensive information, in recent years, the use of financial statement data for bank risk integration has become a direction to solve the problem of lack of risk data. In practice, the bank risks come from the daily business, and the banks’ financial statements record the bank's operation. Therefore, the information contained in the financial statements is an important data source of external personnel to study bank risk. Therefore, more and more researchers collect risk data from financial statements to measure the total bank risk and put forward a series of bank risk aggregation methods based on financial statements. Thus, this chapter reviews and summarizes the existing bank risk aggregation approaches based on financial statements data and further puts forward a framework of financial statements-based bank risk aggregation. Besides, we introduce the basic idea and the general procedure of applying the financial statements-based bank risk aggregation framework.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 4. Bank Risk Aggregation Based on Income Statement
Abstract
This chapter aggregates bank risks based on data collected from financial statements, which solves the problem of data shortage in risk integration to a certain extent. In the empirical analysis, we aggregate credit, market, liquidity and operational risks based on income statements of 16 early-listed Chinese commercial banks for 2007–2018. Empirical results show that at 99.9% confidence level, the total risk VaR values are 0.82%, 0.70% and 0.36% for the large, general and small banks, respectively, indicating that the total risk increases with the decrease of bank size.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 5. A “Factor-Integral” Approach to Solve the Low-Frequency Problem of Income Statement Data
Abstract
This chapter proposes a new risk aggregation approach called the “factor-integral” approach to aggregating credit and market risks. This approach transforms the aggregation of low-frequency risk data into the integral of high-frequency risk factor data. Thus, it can address the problem of risk data shortage in risk aggregation and obtain a more accurate and stable integrated loss distribution. Specifically, credit and market risks are commonly affected by some risk factors. They are naturally correlated with their underlying common risk factors. Therefore, through multiple integrals of high-frequency risk factors, the integrated loss distribution can be constructed. Our proposed approach has been implemented in the Chinese banking sector. Empirical results indicate that the integrated economic capital derived from the factor-integral approach attains a 25% increase compared with the risk aggregation approach characterized by low-frequency risk data.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 6. A Two-Stage General Approach Based on Financial Statements Data and External Loss Data
Abstract
This chapter proposes a novel two-stage bank risk aggregation approach to aggregate three main bank risks (credit, market and operational risks). Compared with previous risk aggregation approaches, the proposed approach can effectively aggregate multiple bank risks more accurately than previous risk aggregation approaches. Specifically, credit and market risks that have common risk factors are aggregated in the first stage by collecting risk data from financial statements. The aggregate risk obtained in the first stage and the operational risk with no common risk factors are aggregated in the second stage to arrive at the total risk. The data of credit and market risks are collected from financial statements. The data of operational risk are collected from an external loss database. Thus, the proposed two-stage bank risk aggregation approach is based on data from financial statements and the external loss database. The proposed approach is empirically compared with three commonly used risk aggregation approaches by applying them to the Chinese banking system to aggregate credit, market and operational risks.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 7. Bank Risk Aggregation Based on Income Statement and Balance Sheet
Abstract
Financial statements record the operating performance of banks that face a variety of risks. Specifically, assets recorded in a balance sheet are risk exposures of banks. Items recorded in income statements are profit and loss (P&L) of banks in their daily business by taking risks. Thus, by mapping balance sheet and income statement items into multiple risk types separately, we can get risk exposure and risk P&L respectively to aggregate bank risks. Thus, this chapter introduces the bank risk aggregation approach based on income statements and balance sheets. In the empirical analysis, we apply this approach to aggregate the credit, market, liquidity and operational risks of the Chinese banking sector based on 16 early Chinese listed commercial banks spanning 2007–2018. Furthermore, we analyze the difference of bank risks in banks of different sizes by constructing a large, general and small Chinese commercial bank. The empirical results show that at 99.9% confidence level, the large bank may suffer an annual total loss of 1159 billion CNY, and the annual loss of the general bank is 1339 billion CNY while the small bank’s annual total loss is equal to 95 billion CNY in 2018.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 8. Bank Risk Aggregation with Off-Balance Sheet Items
Abstract
Recently, researchers have found that mapping financial statements into risk types is a satisfactory way to resolve the problem of data shortage and inconsistency. Nevertheless, ignoring off-balance sheet (OBS) items has so far been regarded as the usual practice in risk aggregation, which may lead to deviations in conclusions. Hence, we improve the financial statements based risk aggregation framework by mapping OBS items into risk types. Based on 743 quarterly financial statements from all 16 early-listed Chinese commercial banks over 2007–2018, we empirically study whether the overall impact of OBS activities on total risk depends on bank size. Moreover, this chapter divides the sample into two subsets, during and after the subprime crisis, to find out how the subprime crisis affects the risks of Chinese banks. Our empirical results show that OBS credit risk is positively linked to the total risk of both large banks and small banks. Thus, the off-balance sheet business increases the total risk of banks regardless of the bank size. The increase in the total risk of large banks due to off-balance sheet business is more significant than that of small banks. Besides, we also found that it is the increase of liquidity risk and market risk that leads to the larger total risk of Chinese banks during the subprime crisis.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 9. Analysis of Textual Risk Disclosures in Financial Statements
Abstract
Qualitative textual risk disclosures reported in financial statements contain massive and comprehensive risk information. Thus, this chapter aims to discover bank risk factors from textual risk disclosures comprehensively. To obtain a more accurate classification result of bank risk factors, we propose a new semi-supervised text mining naive collision approach. We discover 21 risk factors affecting bank risks and 4 of them are unique to the banking industry. Furthermore, the cumulative importance of the top 8 important risk factors account for over 80%, and 3 risk factors become increasingly important for commercial banks’ risk profiles.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 10. Bank Risk Aggregation with Forward-Looking Textual Risk Disclosures
Abstract
Previous studies only used numerical data recorded in financial statements to aggregate bank risk. Time lags in numerical data may bias risk aggregation results. Thus, this chapter innovatively incorporates forward-looking textual risk disclosures reported in financial statements into bank risk aggregation. In doing so, we overcome the drawback of using only historical numerical data, and in turn, we obtain more reasonable aggregate risk results. In our experiment, based on 812 pieces of numerical risk data for each type of credit, market and operational risks and 36,178 summary headings of textual risk disclosures drawn from 1224 Form 10-K filings of 153 U.S. commercial banks for 2010–2017, we aggregate credit, market and operational risks for U.S. commercial banks. In comparing total risks with and without forward-looking textual risk disclosures, our empirical results show that disregarding forward-looking textual risk disclosures overestimates the total risk of 2010–2013 while underestimating the total risk of 2014–2017.
Jianping Li, Lu Wei, Xiaoqian Zhu
Chapter 11. Main Conclusions and Future Research
Abstract
This book propose a financial statements based bank risk aggregation framework. Based on this framework, main bank risks of Chinese banking industry and American banking industry is aggregated by mapping financial statement data and different bank risk types. However, the present framework is not perfect and needs future improvements. Especially in the era of big data, besides financial statements, various types of financial texts, such as credit rating reports, online financial news, financial analyst reports, contain a large amount of information on bank risks, which provides a new perspective and more sufficient information for bank risk analysis. Thus, this chapter summarizes the main research conclusions of this book and put forward some ideas for improving the proposed framework in the future. 
Jianping Li, Lu Wei, Xiaoqian Zhu
Metadaten
Titel
Financial Statements-Based Bank Risk Aggregation
verfasst von
Jianping Li
Assist. Prof. Lu Wei
Xiaoqian Zhu
Copyright-Jahr
2022
Verlag
Springer Singapore
Electronic ISBN
978-981-19-0408-0
Print ISBN
978-981-19-0407-3
DOI
https://doi.org/10.1007/978-981-19-0408-0