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.
In this session, we explore analytics and data that life insurance companies can use to assess the current state of credit portfolios, considering loss, downgrade risk, RBC as well as that consider severity and length of this unprecedented economic slowdown across industries, while also accounting for government reaction and targeted fiscal policies.
This session will highlight:
• The state of credit markets and implications for credit losses, OTTI and RBC
• Preparing for the future - assessing portfolio and cross-industry dynamics along epidemiologic and economic paths
• Assessing the cross-industry impact of targeted fiscal policies (e.g., airline bailout, payments to individuals) and scenarios that explore real-time disruptions such as oil price shocks or COVID-19 impacts
• Presentation Slides
• Paper: Navigating Credit Beyond COVID-19
• Paper: COVID-19 and Beyond: The New Norm of Credit Analytics and Data
Moody's Analytics Managing Director Amnon Levy, Moody's Analytics Director Libor Pospisil, and Moody's Investor's Service Jim Hempstead presented at the International Association of Credit Portfolio Managers Spring Conference entitled Managing Credit Risk and Emerging Threats: Lessons from the Gaps Revealed by the Pandemic.
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.
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.
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.
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.
While bankers are increasingly managing risks related to changes in policy and technology (also known as transition risk), physical risks are not necessarily an obvious set of primary factors for banks’ commercial credit portfolio managers originating credit with maturities of three to seven years.
The initial intent of the CECL guidelines was to make loan-loss allowances more reactive to the credit environment. By setting aside greater allowances, organizations would be better prepared for a default.