Point-in-time
European regulators turn up the heat on IFRS 9 model overlays
After warnings from EBA and BoE, risk managers urge ‘soul-searching’ on post-model adjustments
Estimating correlation parameters in credit portfolio models under time-varying and nonhomogeneous default probabilities
Calibration of rating grades to point-in-time and through-the-cycle levels of probability of default
Backtesting of a probability of default model in the point-in-time–through-the-cycle context
Finding the corporate credit cycle for IFRS 9
Decomposing corporate default rates helps identify credit cycles
IFRS 9 compliant economic adjustment of expected credit loss modeling
This paper presents an International Financial Reporting Standard 9 (IFRS 9) compliant solution related to expected credit loss modeling.
Banks eye post-pandemic shake-up of op risk scenarios
Firms seek better handle on impact of global shocks, and hope to avert regulatory attention
ESG investing: It’s not just great to be good
Investing according to environmental, social and governance (ESG) criteria can be done in various ways, with continuing development of filters and ways of analysing companies. As the market in ESG indexes and investments linked to sustainability matures,…
Libor transition and implementation – Covering all bases
Sponsored Q&A
On probability of default and its relation to observed default frequency and a common factor
This paper considers a definition of through-the-cycle as independent from an economic state that can result in a time-varying TTC probability of default.
On the mathematical modeling of point-in-time and through-the-cycle probability of default estimation/ validation
In this paper, the authors focus on PD estimation and validation. They provide the mathematical modeling for both point-in-time (PIT) and through-the-cycle (TTC) PD estimation, and discuss their relationship and application in our banking system.
Profit emergence under IFRS 17
Major changes are expected under the new IFRS 17 regime – insurance companies must make efforts to comprehend and communicate the full impact of changes to profit emergence under different scenarios, and its sensitivity to different methodology choices,…
Credit data: the Trump effect on PDs
The war on coal is over, according to the US president – and the effect can be seen in banks' default estimates
Credit risk models can dodge procyclical bias – Fed adviser
Excluding some metrics makes A-IRB retail portfolio risk model more stable
Forward ordinal probability models for point-in-time probability of default term structure: methodologies and implementations for IFRS 9 expected credit loss estimation and CCAR stress testing
This paper proposes an ordinal model based on forward ordinal probabilities for rank outcomes.
Volatility of IFRS 9 loss estimates alarms lenders
Accounting model outputs wildly out of sync with those used to calculate regulatory capital requirements
Point-in-time probability of default term structure models for multiperiod scenario loss projection
The author of this paper proposes a dynamic PD term structure model for multi-period stress testing and expected credit loss estimation.
Risk Chartis Market Report: IFRS 9
Sponsored by Oracle, Moody's Analytics and AxiomSL
Some options for evaluating significant deterioration under IFRS 9
The authors of this paper address some issues to do with IFRS 9 and explain how to determine if an instrument has suffered serious deterioration in credit risk.
A point-in-time–through-the-cycle approach to rating assignment and probability of default calibration
This paper proposes a methodology for constructing TTC rating grades and assessing the resulting degree of PIT-ness.
AERB: developing AIRB PIT–TTC PD models using external ratings
In this paper, the authors show how one can use a certain class of models for modeling portfolios such as large corporates, banks and insurance companies.
Biased benchmarks
The authors of this paper contend that recent evidence indicates that benchmarks have, over the last eleven years, exaggerated default risk for nonfinancial corporate entities.