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    Amnon Levy

    Financial researcher; authority in credit portfolio management and AI/ML, risk-based pricing, climate and credit, CECL/IFRS 9; credit and ALM

    Amnon Levy heads the team responsible for model development and quantitative services related to portfolio, balance sheet, and impairment solutions. His current research focuses on the impact of regulations, new accounting standards, and climate on credit valuation and portfolio management. Amnon’s interests also include credit modeling in asset liability management, and using artificial intelligence and machine learning in portfolio strategies.

    Northwestern University, Kellogg Graduate School of Management: PhD, Finance
    University of California, Berkeley: BA, Economics
    Moody's Analytics | Credit Economic Capital

    Credit Economic Capital: Gain insights to manage credit risk, support regulatory compliance, and make active asset allocation decisions.

    Moody's Analytics | Operational Risk Audit Compliance Training

    Portfolio Optimization: Quantify diversification benefits across portfolios and define risk types that inform risk management and active asset allocation decisions.

    Moody's Analytics | Stress Testing

    Stress Testing: Moody’s Analytics helps financial institutions develop collaborative, auditable, repeatable, and transparent stress testing programs to meet regulatory demands.


    Portfolio Models: Models that enable portfolio managers to assess and optimize portfolio risk.

    Asset Valuation: Process of determining the fair market or present value of assets using book values.

    Stress Testing: Gauge of how certain stressors will affect a company, industry, or specific portfolio.

    Credit Correlations: Measurement of whether risky assets are more likely to default together or separately.

    Representative Projects

    Amnon has been published in the Journal of Financial Economics, Journal of Monetary Economics, Encyclopedia of Quantitative Finance, Journal of Banking and Finance, and the Journal of Risk Model Validation. He has also published chapters in CCAR and Beyond - Capital Assessment, Stress Testing and Applications; and The New Impairment Model Under IFRS 9 and CECL.

    Amnon is co-editor of "Credit Risk Measurement and Management: Disruption and Evolution" published by Risk Books.

    Published Work

    Loan Valuation and Credit Portfolio Management Post-IFRS 9, CECL

    RAROC and RORAC solutions that account for allowance and forward-looking IFRS 9 / CECL measures in return and risk.

    November 2019

    Earnings Volatility, Share Price Performance, and Credit Portfolio Management Under CECL and IFRS 9

    This paper studies how earnings volatility induced by credit risk can impact share price performance for financial institutions under CECL and IFRS 9, and quantifies the benefit of an active credit risk management practice.

    April 2019

    Moody's Analytics Webinar: Credit Earnings Volatility and Share Price Performance: Implications of IFRS 9 and CECL

    The new accounting standards can have material implications for allowance and earnings dynamics. Join our researchers, Amnon Levy and Pierre Xu, explore a large sample of banks to better understand channels by which the standards affect shareholder value.

    February 2019

    A Composite Capital Measure Unifying Business Decision Rules in the Face of Regulatory Requirements Under New Accounting Standards

    This paper introduces an approach that quantifies the additional capital buffer an institution requires, beyond the required regulatory minimum, to limit the likelihood of a capital breach.

    May 2018

    Measuring and Managing the Impact of IFRS 9 and CECL Requirements on Dynamics in Allowance, Earnings, and Bank Capital

    This paper explores how CECL and IFRS 9 might impact loss allowance, earnings, and capital dynamics, and how these dynamics might affect credit portfolio management.

    April 2018

    Economic Capital Model Validation: A Comparative Study

    Using a long history of public firm defaults from Moody's Investor Services and Moody's Analytics, this study illustrates a validation approach for jointly testing the impact of PD and correlation upon model performance. We construct predicted default distributions using a variety of PD and correlation inputs and examine how the predicted distribution compares with the realized distribution. The comparison is done by looking at the percentile of realized defaults with respect to the predicted default distribution. We compare the performance of two typical portfolio parameterizations: (1) a through-the-cycle style parameterization using agency ratings-based long-term average default rates and Basel II correlations; and (2) a point-in-time style parameterization using public EDF credit measure, and Moody's Analytics Global Correlation Model (GCorr™). Results demonstrate that a through-the-cycle style parameterization results in a less conservative view of economic capital and substantial serial correlation in capital estimates. Results also show that when point-in-time measures are used, the tested economic capital model produces consistent and conservative economic capital estimates over time. A version of this paper appears in the Journal of Risk Model Validation, March 2013.

    February 2018