| 书目名称 | Practical Credit Risk and Capital Modeling, and Validation | | 副标题 | CECL, Basel Capital, | | 编辑 | Colin Chen | | 视频video | | | 概述 | Offers a guide on credit risk and capital modeling and validation for CECL, IFRS9, Basel Capital and CCAR.Features innovative and real-world techniques and practices with code and examples.Includes te | | 丛书名称 | Management for Professionals | | 图书封面 |  | | 描述 | .This book provides professionals and practitioners with a comprehensive guide on credit risk modeling, capital modeling, and validation for Current Expected Credit Loss (CECL), International Financial Reporting Standard 9 (IFRS9), Basel Capital and Comprehensive Capital Analysis and Review (CCAR) procedures. It describes how credit risk modeling, capital modeling, and validation are done in big banks with code and examples. The book features innovative concepts such as Binary Logit Approximation (BLA) for Competing Risk Framework; Adaptive and Exhaustive Variable Selection (AEVS) for automatic modeling; Full Observation Stratified Sampling (FOSS) for unbiased sampling; and Prohibited Correlation Index (PCI) for Fair Lending Texts. It also features a chapter on credit underwriting and scoring, addressing the credit underwriting risk with some innovations. It is a valuable guide for professionals, practitioners and graduate students in risk management. . | | 出版日期 | Book 2024 | | 关键词 | Credit Model; Adaptive and Exhaustive Variable Selection (AEVS); ACL; Credit Risk; Model Validation; Curr | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-52542-1 | | isbn_softcover | 978-3-031-52544-5 | | isbn_ebook | 978-3-031-52542-1Series ISSN 2192-8096 Series E-ISSN 2192-810X | | issn_series | 2192-8096 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
| 1 |
,Introduction to Credit Risk and Capital Management Frameworks, |
Colin Chen |
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Abstract
is the possibility of a loss resulting from the failure by a borrower, or more generally an obligor, to repay a loan or meet contractual obligations. In banking and financial industries, it refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for collection.
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| 2 |
,Introduction to Credit Risk and Capital Management Frameworks, |
Colin Chen |
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Abstract
is the possibility of a loss resulting from the failure by a borrower, or more generally an obligor, to repay a loan or meet contractual obligations. In banking and financial industries, it refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for collection.
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| 3 |
,Credit Data and Processing, |
Colin Chen |
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Abstract
refer to any data related to a credit product during its lifetime. Credit data vary with different products, for example, retail and wholesale loans have different types of credit data. Credit data also depends on availability by aggregation levels. Account-level data have more granular information for each account compared to aggregated cohort-level data. In the time horizon, credit data can be classified as origination data and transaction data. While the origination data describe all characteristics at the credit product origination, the transaction data record the periodic (e.g., daily, weekly, monthly) status changes of the credit product. So, the origination data are static, and the transaction data are dynamic. In the content horizon, origination data includes feathers describing borrower/obligor characteristics, product characteristics, and collateral characteristics if the product is secured by some underlying asset. For transaction data, it could consist of the full transaction history since origination, or transactions from a booking date, or just a snapshot of the booking at some specific dates. Figure 2.1 shows the structures of credit data by these different dimensions.
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| 4 |
,Credit Data and Processing, |
Colin Chen |
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Abstract
refer to any data related to a credit product during its lifetime. Credit data vary with different products, for example, retail and wholesale loans have different types of credit data. Credit data also depends on availability by aggregation levels. Account-level data have more granular information for each account compared to aggregated cohort-level data. In the time horizon, credit data can be classified as origination data and transaction data. While the origination data describe all characteristics at the credit product origination, the transaction data record the periodic (e.g., daily, weekly, monthly) status changes of the credit product. So, the origination data are static, and the transaction data are dynamic. In the content horizon, origination data includes feathers describing borrower/obligor characteristics, product characteristics, and collateral characteristics if the product is secured by some underlying asset. For transaction data, it could consist of the full transaction history since origination, or transactions from a booking date, or just a snapshot of the booking at some specific dates. Figure 2.1 shows the structures of credit data by these different dimensions.
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| 5 |
,Credit Modeling Techniques, |
Colin Chen |
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Abstract
Credit risk modeling techniques become mature over more than a half century of developments. While modeling for credit risk could be traced back much earlier, theoretical affirmation of statistical models, for example, the multinomial logit model as a special case of the more general conditional logit model, was first provided about a half century ago (McFadden, 1974) using the random utility maximization paradigm. Since then, statistical models like the generalized linear models (GLM) have become the most popular selection in modeling credit risks, though machine learning models start to challenge that dominance in some areas in recent years. Figure 3.1 outlines the structure of various models discussed in this chapter.
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| 6 |
,Credit Modeling Techniques, |
Colin Chen |
|
Abstract
Credit risk modeling techniques become mature over more than a half century of developments. While modeling for credit risk could be traced back much earlier, theoretical affirmation of statistical models, for example, the multinomial logit model as a special case of the more general conditional logit model, was first provided about a half century ago (McFadden, 1974) using the random utility maximization paradigm. Since then, statistical models like the generalized linear models (GLM) have become the most popular selection in modeling credit risks, though machine learning models start to challenge that dominance in some areas in recent years. Figure 3.1 outlines the structure of various models discussed in this chapter.
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| 7 |
,Allowance for Credit Loss and CECL, |
Colin Chen |
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Abstract
In this chapter, we focus on the allowance for credit loss or ACL, especially with CECL, by applying the theories and methodologies in the previous chapters on both retail and wholesale portfolios.
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| 8 |
,Allowance for Credit Loss and CECL, |
Colin Chen |
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Abstract
In this chapter, we focus on the allowance for credit loss or ACL, especially with CECL, by applying the theories and methodologies in the previous chapters on both retail and wholesale portfolios.
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| 9 |
,Capital Management and Risk Weighted Asset, |
Colin Chen |
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Abstract
This chapter is a continuation of Sect. . on capital management by focusing on the credit products. Following the framework introduced in Sect. ., we cover both regulatory capital and economic capital. For both areas, we will go deeper into the modeling techniques introduced in Chap. . and show how those techniques are implemented in capital calculation.
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| 10 |
,Capital Management and Risk Weighted Asset, |
Colin Chen |
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Abstract
This chapter is a continuation of Sect. . on capital management by focusing on the credit products. Following the framework introduced in Sect. ., we cover both regulatory capital and economic capital. For both areas, we will go deeper into the modeling techniques introduced in Chap. . and show how those techniques are implemented in capital calculation.
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| 11 |
,Stress Test and CCAR, |
Colin Chen |
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Abstract
This chapter covers credit risk and capital modeling in stress test. While stress test is a broad topic, we will focus on some practical stress test frameworks – the regulatory stress test framework and the systematic stress test framework. As introduced in Chap. ., the regulatory stress test framework was created due to the DFAST and implemented in the annual CCAR process for participant institutions. The systematic stress test framework is used by some larger institutions for their internal risk management purposes. In addition, a bottom-up risk integration framework like the conditional economic capital framework described in the previous chapter can also be used for stress test purpose, especially for reverse stress test. For all these frameworks, credit risk is one component, most often one critical component. We will illustrate how credit risk modeling is carried out in each of these frameworks, as well as how these modeling results are used in risk management and reporting.
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| 12 |
,Stress Test and CCAR, |
Colin Chen |
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Abstract
This chapter covers credit risk and capital modeling in stress test. While stress test is a broad topic, we will focus on some practical stress test frameworks – the regulatory stress test framework and the systematic stress test framework. As introduced in Chap. ., the regulatory stress test framework was created due to the DFAST and implemented in the annual CCAR process for participant institutions. The systematic stress test framework is used by some larger institutions for their internal risk management purposes. In addition, a bottom-up risk integration framework like the conditional economic capital framework described in the previous chapter can also be used for stress test purpose, especially for reverse stress test. For all these frameworks, credit risk is one component, most often one critical component. We will illustrate how credit risk modeling is carried out in each of these frameworks, as well as how these modeling results are used in risk management and reporting.
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| 13 |
,Underwriting and Credit Scoring, |
Colin Chen |
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Abstract
In the previous three chapters, we deal with credit risk modeling for loans and portfolios already in house. For financial institutions in the origination business, there is a critical credit risk at the door when doing underwriting, since the quality of the loans approved and funded will decide those risks we discussed in the previous chapters or the price if these loans are sold. The credit risk in underwriting is traditionally treated independently from the credit risks in the common credit risk management frameworks we presented early given that not all financial institutions carry origination businesses for products in their portfolios and credit risk embedded in underwriting is more considered business strategic risk or market risk from the underwriting line of businesses. Nevertheless, such risks are measured by credit events, and a similar credit risk modeling technique called credit scoring is dominantly used.
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| 14 |
,Underwriting and Credit Scoring, |
Colin Chen |
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Abstract
In the previous three chapters, we deal with credit risk modeling for loans and portfolios already in house. For financial institutions in the origination business, there is a critical credit risk at the door when doing underwriting, since the quality of the loans approved and funded will decide those risks we discussed in the previous chapters or the price if these loans are sold. The credit risk in underwriting is traditionally treated independently from the credit risks in the common credit risk management frameworks we presented early given that not all financial institutions carry origination businesses for products in their portfolios and credit risk embedded in underwriting is more considered business strategic risk or market risk from the underwriting line of businesses. Nevertheless, such risks are measured by credit events, and a similar credit risk modeling technique called credit scoring is dominantly used.
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发表于 2025-3-21 19:02:58
