With COVID-19 continuing to batter the global economy, many banks are struggling to model credit losses as they prepare for their upcoming Comprehensive Capital Analysis and Review (CCAR) submissions as well as 3rd Quarter earnings.
This discussion will focus on the following themes:
- Models that calibrate the sensitivity of credit losses using the last 20 years of pre-COVID data simply will not pick up on the varying degrees to which different industries are affected by COVID-19
- Introducing alternative data and analytics to describe COVID induced cultural shifts, and assessing the profound impact of COVID on cross-industry-country credit risk
- How further fiscal and monetary stimulus programs distance the current environment from being properly described by the historical relationships between credit quality across industry segments and macroeconomic variable
- The extreme nature of economic scenarios are often out of model range, and not representative of the credit environment
- Introduce alternative data and analytics to describe COVID induced cultural shifts
- Assess the profound impact of COVID on cross-industry-country credit risk
- Discuss the use and interpretation of extreme economic scenarios
High-level overview of the modeling methodologies implemented in RiskFrontier™ and their business applications. RiskFrontier calculates a credit investment's value at analysis date, its value distribution at a user-specified investment horizon, and its marginal contribution to portfolio risk, for every instrument in the portfolio.
We study the impact of COVID on concentration risk, relevant in the context of limit-setting, portfolio allocation, and other concentration-sensitive measures. Analysing a European portfolio, we show how our solutions can be used to navigate the COVID crisis and better understand risk within a portfolio framework.
Crises reveal behavior incongruent to historic patterns, requiring new data and analyses. COVID shows established models did not evaluate credit adequately. The Cross-Sectional COVID Overlay assesses current credit, projected ratings, and loss measures in new ways, anchoring to well-understood starting points and scenarios.
We introduce a granular, obligor-level, scenario-based model for rating transition matrices. It recognizes differences in the statistical properties of ratings and forward-looking PDs, deviating from approaches assuming a one-to-one relationship between segment rating and PD or that decouple dynamics of ratings and PDs.
Well-established models that evaluate the current credit environment are not working given COVID-19. Internal ratings cannot update at frequencies required to react well. This paper addresses these challenges, presenting applications users can incorporate into Internal Rating Assessment and Projected Ratings and Loss.
The COVID-19 pandemic has brought credit risks that are unprecedented in size, are fast-changing, and have vastly different manifestations across industries. The uncertainty of impact is driven by epidemiological progression and sociological response, balanced by fiscal and monetary stimulus.
Some Small and Medium-Sized Enterprises (SMEs) in the UK and beyond will have enough working capital relative to fixed expenses to withstand an extended business closure, but many will need help.
COVID-19 created additional complexities for institutions navigating CECL accounting standard. This paper provides a natural quantitative approach for incorporating concentration in the allowance process and portfolio management.
In this webinar we discuss how some Small and Medium Sized Enterprises (SMEs) will have enough working capital relative to fixed expenses to withstand an extended business closure, but many will need help.
COVID-19 will have far reaching effects on the accounting for CECL and IFRS 9.