Yashan works with global clients, providing training and advice on enterprise risk management, impairment, asset and liability management, and stress testing. Prior to joining Moody's Analytics, Yashan was an assistant professor at the MIT Sloan School of Management. He has a PhD in Management Science from Columbia University.
Learn to differentiate C&I, CRE, retail, and securities. Choose approaches at the right level of flexibility and sophistication. Apply model-free solutions based on historical internal or industry data.
For insurers, including reinsurance receivables is a unique result of the CECL accounting standard.
This paper explores the CECL standard's background, the choices community banks, regional banks, and credit unions face, and some suggested approaches for dealing with these challenges.
In this article, we use historical data to calculate and compare loan- and portfolio-level loss allowances under the incurred loss model and CECL.
This paper investigates the impact of using EDF9 instead of EDF8 values as inputs for estimating credit portfolio risk measures within Moodys Analytics RiskFrontier®. The recent EDF9 enhancements affect portfolio risk analysis via various channels — due not only to new values for default probabilities, but also because the market Sharpe ratio (i.e. market-level risk premium) and asset return-based correlations for corporate exposures depend on time series of EDF measures. In this paper, we focus on the question of how using the new EDF9 default probabilities alter patterns in portfolio risk measures.
IFRS 9 materially changes how institutions set aside loss allowance. With allowances flowing into earnings, the new rules can have dramatic effects on earnings volatility. In this paper, we propose general methodologies to measure and manage credit earnings volatility of a loan portfolio under IFRS 9. We walk through IFRS 9 rules and the different mechanisms that it interacts with which flow into earnings dynamics. We demonstrate that earnings will be impacted significantly by credit migration under IFRS 9. In addition, the increased sensitivity to migration will be further compounded by the impact of correlation and concentration. We propose a modeling framework that measures portfolio credit earnings volatility and discuss several metrics that can be used to better manage earnings risk.