Stress Testing Banks

This reading is a part of the syllabus for FRM Part 2 Exam in the section ‘Operational and Integrated Risk Management’.

How much capital and liquidity does a bank need – to support its risk taking activities? During the recent (and still ongoing) financial crisis, answers to this question using standard approaches, e.g. regulatory capital ratios, were no longer credible, and thus broad-based supervisory stress testing became the new tool. Bank balance sheets are notoriously opaque and are susceptible to asset substitution (easy swapping of high risk for low risk assets), so stress tests, tailored to the situation at hand, can provide clarity by openly disclosing details of the results and approaches taken, allowing trust to be regained. With that trust re-established, the cost-benefit of stress testing disclosures may tip away from bank-specific towards more aggregated information. This still provides the market with unique information (supervisors, after all, have access to proprietary bank data) without dis-incentivizing market participants from producing private information and trading on it – with all the downstream benefits of information-rich prices and market discipline.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.