Senior Director, Research
Dr. Yashan leads the research and quantitative modeling team for portfolio valuation, accounting, and balance sheet analytics. The team develops analytic and empirical models for asset valuation, IFRS 9/CECL, PPNR, and ALM.
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.
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.
IFRS 9 aims to streamline and strengthen risk measurement and reporting of financial instruments in an efficient, forward-looking manner. This new accounting standard will have far-reaching impacts on accounting practices and performance results.
This paper introduces a framework for stress testing portfolios of credit risk sensitive securities. Specifically, the framework uses a macroeconomic scenario to project stressed expected losses (EL) on the securities by accounting for credit quality changes, recovery risk effects, fluctuations in market price of risk, and interest rates paths. The calculations are carried out analytically over multiple periods.
Modeling the Joint Credit-Interest Rate Dynamics on a Multi-Dimensional Lattice Platform: Model Validation and Applications in Risk Integration
This document presents validation results for the credit-interest lattice or the multi-dimensional lattice (MDL) valuation model within Moody's Analytics RiskFrontier™.
Moody's Analytics RPC Presentation: Levy Wang on Assessing and Pricing Liquidity Risk
(A version of this has been published in the Journal of Banking & Finance, 2011, vol. 35, issue 12, pages 3145-3157) Credit migration is an essential component of credit portfolio modeling. In this paper, we outline a framework for gauging the effects of credit migration on portfolio risk measurements.
This paper discusses the implications of the Moody's Analytics PD-LGD correlation model on portfolio analysis. We provide numerical results to illustrate the impacts of PD-LGD correlation on risk and return measures of credit portfolios.
While the sophistication and adoption of the data, models, and software systems for individual risk types has become more widespread, the tools for consistently measuring integrated risk lag. Typically, individual risk components are aggregated in ways ranging from simple summation to employing copula methods that describe the relationship between risk types. While useful, these “top-down” approaches are limited in their ability to describe the interactive effects of various risk factors that drive loss.