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Dr. Amnon Levy heads the group responsible for research development and quantitative services related to Moody’s Analytics portfolio and balance sheet solutions.

Amnon has a BA in economics from the University of California at Berkeley and a PhD in finance from the Kellogg Graduate School of Management, Northwestern University. Prior to joining Moody’s Analytics, he was a visiting assistant professor at the Stern School of Business, New York University, and the Haas School of Business, University of California at Berkeley. He has also taught corporate finance at the Kellogg School of Management, Northwestern University, and worked at the Board of Governors of the Federal Reserve System. He is currently teaching a course on credit risk in the Haas School of Business MFE program.

Amnon has been published in the Journal of Financial Economics, the Journal of Monetary Economics, the Encyclopedia of Quantitative Finance, the Journal of Banking and Finance, and the Journal of Risk Model Validation. His current research interests include the impact of credit in ALM, and unifying the management of regulatory capital, economic risks, and the impact of accounting rules.

Related Insights
Whitepaper

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

Prudent credit risk management ensures institutions maintain sufficient capital and limit the possibility of a capital breach. With CECL and IFRS 9, the resulting trend toward greater credit earnings volatility raises uncertainty in capital supply, ultimately causing an increase in required capital. It is ever more challenging for institutions to manage their top-of-the house capital while steering their business to achieve the desired performance level. 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. In addition, we introduce a new measure that allocates capital and recognizes an instrument's regulatory capital requirements, loss allowance, economic concentration risks, and the instrument's contribution to the uncertainty in capital supply and demand. In-line with the Composite Capital Measure introduced in Levy and Xu (2017), this extended measure includes far-reaching implications for business decisions. Using a series of case studies, we demonstrate the limitations of alternative measures and how institutions can optimize performance by allocating capital and making business decisions according to the new measure.

May 2018 Pdf Dr. Amnon Levy, Xuan Liang, Dr. Pierre Xu
Whitepaper

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

Reserving for loan loss is one of the most important accounting aspects for banks. Its objective is to cover estimated losses on impaired financial instruments due to defaults and non-payment. Reserve measurement affects both the balance sheet and income statement. It impacts earnings, capital, dividends and bonuses, and attracts the attention of bank stakeholders ranging from the board of directors and regulators to equity investors. In response to the so-called “too-little, too-late” problem experienced with loan loss reserve during the Great Financial Crisis, accounting standard setters now require that banks provision against loan loss based on expected credit losses (ECL). Arguably, calculating the Expected Credit Loss Model under IFRS 9 and CECL presents a momentous accounting change for banks, with the new standards coming into effect sometime between 2018 and 2021, depending on the jurisdiction.

March 2018 Pdf Dr. Amnon Levy, Dr. Jing Zhang
Whitepaper

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 Pdf Zhenya Hu, Dr. Amnon Levy, Dr. Jing Zhang