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CCAR and Beyond: Capital Assessment, Stress Testing and Applications

CCAR and Beyond: Capital Assessment, Stress Testing and Applications

Author: Jing Zhang
Publisher: Risk Books
ISBN: 9781782720829
Published In: December 2013
Format: Paperback , 404 pages
Jurisdiction: International ? Disclaimer:
Countri(es) stated herein are used as reference only

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Author

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Following the global financial crisis, the Comprehensive Capital Analysis Review – CCAR – has emerged in the US as the most dominant regulatory regime to face banks in recent years: banks must be able to prove to regulators that they have sufficient capital to weather a severe economic downturn.

CCAR and Beyond is the first authoritative reference guide to CCAR, providing a complete introduction and analysis of the regulation from origins to implementation.

With extensive experience advising CCAR banks on modelling issues, Jing Zhang brings together a cross-section of views from those directly involved in the regulation and implementation of these assessments and stress tests. CCAR and Beyond clarifies the various methodologies and techniques to approaching CCAR assessments, providing unique insight into this seminal regulation.

Since the Supervisory Capital Assessment Program (SCAP) in 2009, the Federal Reserve has refined its expectations on capital assessments and stress tests to form the Comprehensive Capital Analysis Review – CCAR – an annual assessment and stress testing exercise to be performed by banks to rigorously measure whether they have enough capital to withstand another crisis.

The spread and breadth of CCAR continues to expand; originally designed for the largest bank holding companies in the US, the assessments are now being performed at smaller banks and regulators across the globe are eagerly watching the results unfold.

CCAR is widely considered to be the regulation with the greatest influence on banks’ risk management and business practices, mainly due to the approval of dividend issuance, share buy-back, acquisitions and other major corporate actions hinging on the outcome of these assessments.

CCAR and Beyond: Stress Testing, Capital Planning and Implications explores the modelling techniques key to CCAR and the business implications of the programme. Contributions from those directly involved in the implementation and regulation of these assessments provide a unique source of information and insight into the assessment practices.

Jing Zhang brings together industry experts in stress testing and capital assessment to examine the central issues surrounding CCAR including:
- the design and severity of the macroeconomic scenarios;
- C&I and CRE portfolio stress testing;
- market, counterparty and operational risks;
- pre-provision net revenue modelling;
- governance, and
- capital management.
Clarifying the various methodologies and techniques, this book is an essential companion for those implementing and performing these capital adequacy assessments and stress testing exercises.
Introduction
Jing Zhang (Moody’s Analytics)

1.CCAR and Stress Testing as Complementary Supervisory Tools
Tim P. Clark and Lisa H. Ryu (Federal Reserve Board)

2. Financial Institution Perspectives on the Evolving Role of Enterprise-wide Stress Testing
Andy McGee and Ilya Khaykin (Oliver Wyman)

3. The Advancement of Stress Testing at Banks
Michel Araten (Credit Risk Capital Advisory)

4. Designing Macroeconomic Scenarios for Stress Testing
Mark M. Zandi (Moody’s Analytics)

5. Determining the Severity of Macroeconomic Stress Scenarios
Kapo Yuen (Federal Reserve Bank of New York)

6. Data, Analytics and Reporting Requirements: Challenges and Solutions
John P. Haley (Regions) and Thomas Day (Moody’s Analytics)

7. A Multi- view Model Framework for Stress Testing C&I Portfolios
Jimmy Yang and Kenneth Chen (Union Bank)

8. Stress Testing Credit Losses for Commercial Real Estate Loan Portfolios
Jun Chen (Moody’s Analytics)

9. Stress Testing and Retail Portfolios
Soner Tunay and Rosa Català (Citizens Bank)

10. Market and Counterparty Risk Stress Test
Eduardo Canabarro (Morgan Stanley)

11. On Operational Risk Stress Testing
Yakov Lantsman, Sabeth Siddique and Yan Shi (M&T Bank)

12. Quantitative PPNR Modelling
Amnon Levy (Moody’s Analytics)

13. Banks’ Governance and Controls over Internal Capital Adequacy Processes
David Palmer (Federal Reserve Board) and Paul Sternhagen (Federal Reserve Bank of San Francisco)

14. CCAR and Capital Management: Relationship with Economic Capital, Regulatory Capital and ICAAP
Dan Ryan and Pranjal Shukla (PricewaterhouseCoopers)

15. EU-wide Stress Test Versus SCAP and CCAR: Region-wide and Global Perspectives
Piers Haben, Caroline Liesegang and Mario Quagliariello (European Banking Authority)
Jing Zhang is a Divisional Managing Director and the Global Head of Moody’s Analytics Quantitative Research Group. Formerly known as the Moody’s KMV Research Group, the team is responsible for the quantitative modeling behind the EDF and LGD models for both public and private firms, commercial real estate, and portfolio and ALM analytics. Jing joined the KMV research team in 1998, eventually managing day-to-day research operations in 2000. He has made major contributions to a number of KMV quantitative models widely used in the industry today. Jing has also held a number of additional senior roles in Product Management and the Client Solutions Group, and he has advised banks and other financial institutions on risk management issues for many years. He has extensive experience supporting CCAR banks conducting loss estimation, PPNR modeling, model validation, benchmarking and capital planning, stemming from more than a dozen major client projects. Jing obtained his Ph.D. from the Wharton School of the University of Pennsylvania and his Master Degree from Tulane University. He was a lecturer for the Master of Financial Engineering Program at the University of California, Berkeley from 2010–2012. His research papers have been published in both academic and industry journals, such as the Journal of Time Series, the Journal of Fixed Income, and the Journal of Risk Model Validation.