A Multi-view Model Framework for Stress Testing C&I Portfolios
Jimmy Yang and Kenneth Chen
Introduction
CCAR and Stress Testing as Complementary Supervisory Tools
Financial Institution Perspectives on the Evolving Role of Enterprise-wide Stress Testing
The Advancement of Stress Testing at Banks
Designing Macroeconomic Scenarios for Stress Testing
Determining the Severity of Macroeconomic Stress Scenarios
Data, Analytics and Reporting Requirements: Challenges and Solutions
A Multi-view Model Framework for Stress Testing C&I Portfolios
Stress Testing Credit Losses for Commercial Real Estate Loan Portfolios
Stress Testing and Retail Portfolios
Market and Counterparty Risk Stress Test
On Operational Risk Stress Testing
Quantitative PPNR Modelling
Banks’ Governance and Controls over Internal Capital Adequacy Processes
CCAR and Capital Management: Relationship with Economic Capital, Regulatory Capital and ICAAP
EU-wide Stress Test Versus SCAP and CCAR: Region-wide and Global Perspectives
This chapter will focus on a multi-view framework for stress testing commercial and industrial (C&I) portfolio. It will first discuss the challenges for stress testing wholesale portfolio, before introducing loss forecasting methods and a multi-view framework to properly assess and hedge model uncertainties. Besides probability of default (PD), the most important component of loss estimate in an expected loss (EL) framework, we will also explore several common methodologies to stress loss given default (LGD) and exposure at default (EAD). Finally, the chapter will conclude with a discussion of how to combine all these components to finalise recommended results from both a quantitative and qualitative perspective.
CHALLENGES FOR WHOLESALE STRESS TESTING
Estimating loss for the loan book under stress is problematic. On the one hand, the hypothetical scenario has not typically existed in history. Statistical models based on the historical relationship between loss and macroeconomic variables may not have fully reflected “accurate” sensitivity under the new regime. On the other hand, validation of models is also not straightforward. Unlike models used for baseline forecasting, where
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