Managing Director, Head of Portfolio and Balance Sheet Research
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.
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.
Amnon Levy, managing director and head of portfolio and balance sheet research at Moody's Analytics, discusses the evolving expectations of institutions for credit portfolio management, as well as how it is being altered and adapted amid greater impact from new regulatory and technological advancements.
A Composite Capital Allocation Measure Integrating Regulatory and Economic Capital, and the Impact of IFRS 9 and CECL
We propose a composite capital allocation measure integrating regulatory and economic capital. The approach builds upon the economic framework underpinning traditional RORAC-style business decision rules, allowing for an optimized risk-return tradeoff while adhering to regulatory capital constraints. The measure has a number of depictions, and it can be viewed as a weighted sum of economic and regulatory capital, as economic capital adjusted for a regulatory capital charge, or as regulatory capital adjusted for concentration risk and diversification benefits. Intuitively, when represented as economic capital adjusted for a regulatory capital charge, the adjustment can be represented as the additional top-of-the-house regulatory capital, above economic capital, allocated by each instrument's required regulatory capital. We show that the measure has ideal properties for an integrated capital measure. When regulatory capital is binding, composite capital aggregates to the institution's top-of-the-house target capitalization rate. We find the measure is higher than economic capital, but lower than regulatory capital for instruments with high credit quality, reflecting the high regulatory capital charge for this instrument class. Finally, we address how IFRS 9/CECL impacts the CCM and discuss the broader implications of the new accounting standards.
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.
How to Manage the Impact of IFRS 9 on Earnings Volatility and the Supply and Demand of Regulatory Capital
With the implementation of IFRS 9 underway, institutions want to better quantify the impact of IFRS 9 on provisions, result earnings and capital buffers. During this video webinar, we will discuss the strategic impact of IFRS 9 on earnings, capital and investment concentration.
International Financial Reporting Standard 9 (IFRS 9) is a high-impact symbolic, operational, IT and organisational transformation event for finance and risk. The Risk Chartis IFRS 9 Market Report focuses on the key challenges for banks implementing IFRS 9, including exclusive content from Moody's Analytics.
Managing Earnings Volatility and Uncertainty in the Supply and Demand for Regulatory Capital: The Impact of IFRS 9
This paper presents a novel modeling approach that allows for better management of the interplay between supply and demand dynamics for regulatory capital, combining an economic framework with regulatory capital and new loss recognition rules. The framework is particularly relevant in understanding the extent to which IFRS 9 can lead to more aggressive provisioning, which feeds into earnings volatility. Our approach provides guidance on how organizations can better manage their capital buffer, considering investment concentration, its impact on earnings volatility, and the relationship with regulatory capital requirements. Imperative to portfolio management, the framework recognizes the likelihood of a capital shortfall being significantly impacted by portfolio asset class, geography, industry, and name concentration, as extreme fluctuations in capital supply and demand occur more often for institutions holding more concentrated portfolios. Finally, we discuss integrated investment and strategic decision measures that account for the full spectrum of economic risks and interactions with regulatory and accounting rules, as well as instruments' contribution to earnings volatility and capital surplus dynamics.
Banks commonly use Risk Contribution, or contribution to portfolio Unexpected Loss (i.e., standard deviation), as a risk allocation method. While the method has some very desirable properties, it can also produce seemingly counterintuitive dynamics, whereby high interest income-producing assets are associated with higher risk, all else being equal. This dynamic manifests from the higher interest income assets possessing higher value, leading to higher standard deviation in absolute terms. In reality, financial institutions often use interest income to offset losses, and thus, associate higher interest with lower risk. This paper introduces a new, income-adjusted form of Risk Contribution-based capital allocation, designed so that interest income offsets losses. The measure demonstrates improved properties for exposures with particularly high coupons.
Quantitative Research Webinar Series: Modeling Uncertainty in Regulatory Capital and the Impact of IFRS 9 and CECL
Amnon Levy, Managing Director of Portfolio Research at Moody’s Analytics, discusses a novel modeling approach that allows organizations to better manage the supply and demand dynamics for regulatory capital. The approach marries an economic capital (EC) framework with (RegC) and loss accounting rules.
Higher capital standards imposed by new stress testing requirements have forced organizations to address how to better manage capital to meet regulatory constraints. While maintaining higher capital levels is indeed mandatory, simply satisfying the requirement does not necessarily align with stakeholders' preferences for optimal capital deployment and investment decisions. CCAR-style stress tests are requirements that organizations must adhere to; however, these exercises likely do not reflect how stakeholders actually trade off risk and return.
This document presents a credit portfolio stress testing method that analytically determines multi-period expected losses under various macroeconomic scenarios. The methodology utilizes Moody's Analytics Global Correlation Model (GCorr) Macro model within the credit portfolio modeling framework. GCorr Macro links the systematic credit factors from GCorr to observable macroeconomic variables. We describe the stress testing calculations and estimation of GCorr Macro parameters and present several validation exercises for portfolios from various regions of the world and of various asset classes.
When parameterizing an Economic Capital (EC) framework, organizations must consider how losses and gains on principal and coupons/fees are recognized, if they are to ensure appropriate capitalization. The level of loss allowance and capital organizations hold must be sufficient to cover potential losses. This paper outlines how parametrization differs for accrual and securities portfolios. In addition, we relate parametrization approaches with those associated with Basel Advanced-IRB calculations. We conclude that, when measuring an organization's required economic capital buffer, the relevant loss reference point is the accounting value net of loss allowance — losses should be measured in excess of total spread. While seemingly inconsistent with the Basel A-IRB formulation, where losses are measured in excess of expected loss, the difference can be interpreted as loss allowance exactly aligning with expected loss.
In some instances, financial institutions prefer to take longer-term views when assessing the risks of their credit portfolio. While forward-looking or Point-in-Time (PIT) parameters might be more reflective of the current economic environment, their frequent updates may create fluctuations in risk measures, such as economic capital and unexpected loss, which may not be desirable in some applications. This paper outlines two approaches that financial institutions can consider to estimate Through-the-Cycle (TTC) correlation parameters. The first approach averages PIT measures across years to obtain a longer-term TTC average. The second approach calibrates a TTC correlation measure that generates a default distribution in-line with the institution's actual default distribution.
This article outlines recent approaches to managing credit risk when facing regulatory capital requirements. We explore how institutions should best allocate capital and make economically-optimized investment decisions under regulatory capital constraints, such as those imposed by Basel or CCAR-style rules.
In this webinar, Moody’s Analytics will discuss practical considerations when unifying regulatory and economic capital in investment decisions and the method for measuring this unified approach.
The degree to which an organization's regulatory capital is constraining impacts an investment's appeal. The more constraint on the organization, the more heavily an instrument's regulatory capital weighs down the investment's appeal, with investments assigned higher regulatory capital impacted more. This paper explores a method for measuring the extent to which an organization's regulatory capital binds and calibrates the model introduced by Levy, Kaplin, Meng, and Zhang (2012), which unifies regulatory and economic capital in investment decisions. We then examine the impact of the regulatory capital requirement on investment decisions based on the calibrated model. We find that the rank order of exposures' risk-return tradeoff in our sample portfolio changes substantially when taking into account the regulatory capital constraint.
In this paper, we explore leveraging an organization's economic capital framework to quantify the RAS via risk- and macro scenario-based limits.
In this webinar, Moody’s Analytics will discuss practical considerations when unifying regulatory and economic capital in investment decisions and the method for measuring this unified approach.
This paper discusses various quantitative approaches for linking macroeconomic scenarios with PPNR items. Given the broad range of PPNR categories, each item requires special consideration when developing a modeling approach. Modeling approaches range in granularity and depend upon the availability and quality of historical data, statistical properties of the line item, business use and application, and model consistency across balance sheet and income statement items.
Stress Testing Webinar Series: Macroeconomic Conditional Pre-provision Net Revenue (PPNR) Forecasting
This webinar discusses the primary challenges confronting banks when forecasting macroeconomic conditional pre-provision net revenue (PPNR), best practices for forecasting macroeconomic conditional PPNR, and the tools and techniques used by Moody’s Analytics to address the challenges.
Amnon Levy, Managing Director and Head of Portfolio Research at Moody's Analytics, shares solutions to integrating the three kinds of stress testing variables for strategic decision making.
In this presentation, which accompanies a recorded Moody's Analytics webinar of the same title, Dr. Amnon Levy discusses Portfolio Research methodology and findings of the new unified measures (RORAC and EVA™) which allow institutions to rank-order their portfolios and potential deals in a way that accounts for both economic risks and regulatory changes.
Learn about Moody's Analytics Portfolio Research methodology and findings of the new unified measures, which allow institutions to rank-order their portfolios and potential deals in a way that accounts for both economic risks and regulatory changes.
In this paper, we introduce two new measures that incorporate both RegC and EC: return on risk-adjusted capital (RORAC) and economic value added (EVA™). These measures allow institutions to rank-order their portfolios and potential deals in a way that accounts for economic risk and regulatory charges.
This document provides a high-level overview of the modeling methodologies implemented in Moody's Analytics RiskFrontier™. To address the challenges faced by credit risk or credit portfolio managers, RiskFrontier models a credit investment's value at the analysis date, its value distribution at some investment horizon, as well as the portfolio-referent risk of every instrument in the portfolio. The approach is designed to explicitly analyze a wide range of credit investments and contingencies, including term loans with prepayment options and grid pricing, dynamic utilization in revolving lines of credit, bonds with put and call options, equities, credit default swaps, retail instruments, commercial real estate loans, and structured instruments.
This research develops an approach to expand the Moody's Analytics Global Correlation Model (GCorr) to include macroeconomic variables. Within the context of this document, macroeconomic variables can include financial market variables, economic activity variables, and other risk factors. The expanded correlation model, known as GCorr Macro, lends itself to several functions that facilitate a cohesive and holistic risk management practice.
Required economic capital (EC) and regulatory capital (RegC) are two measures frequently used in loan origination and other decisions related to portfolio construction. EC accounts for economic risks such as diversification and concentration effects. When used in measures such as return on risk-adjusted capital (RORAC) or Economic Value Added (EVA™), EC can provide useful insights that allow institutions to optimize risk-return profiles, facilitate strategic planning and limit setting, as well as quantify risk appetite. Meanwhile, when RegC is binding, an institution faces a tangible cost, in that additional capital is needed for new investments that face a positive risk weight. Given these observations, both EC and RegC should influence decision making. After all, a deal with lower RegC but the same EC is favorable, and a deal with lower EC but the same RegC is favorable. In this paper, we formalize RORAC and EVA measures that incorporate both RegC and EC. The new measures allow institutions to rank-order their portfolios and potential deals in a way that accounts for economic risks and regulatory charges.
New Risk Management Techniques that Improve Strategic Planning
Moody's Analytics RPC Presentation: Levy Wang on Assessing and Pricing Liquidity Risk
Portfolio management problem: Benchmarking portfolio to an index is a common problem. A common approach is to track an index with a smaller number of positions in order to minimize transaction costs. Traditionally, more focus has been on the return; Second-moment-based risk measures such as Unexpected Loss (Standard Deviation) are also used; Less consideration is given to higher-moment, e.g., Tail Risk measures, that are important for credit portfolios.
(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.
Assessing credit risk and ensuring the effectiveness and reliability of credit models are critically important to many risk managers and portfolio managers, especially during financial crises. This validation study examines the measurement accuracy of the portfolio credit risk models employed in Moody's Analytics RiskFrontier.
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.
Understanding how the components of asset correlation change through time will allow us to investigate how asset correlation dynamics behave during periods of economic stress. Although the time-varying correlation of equity returns has been extensively researched, we have found few studies on the dynamics of asset correlation over time. In this paper, we explore how both R-squared values and systematic factor correlations change through time. We show that R-squared values are more volatile than the systematic factor correlations. We also study the relationship between changes in R-squared and changes in factor variance, as well as the relationship between changes in factor correlation and changes in factor variance.
This document provides a high-level overview of the modeling methodologies implemented in Moody's KMV RiskFrontier®. To address the challenges faced by credit risk or credit portfolio managers, RiskFrontier models a credit investment's value at the analysis date, its value distribution at some investment horizon, as well as the portfolio-referent risk of every instrument in the portfolio. The approach is designed to explicitly analyze a wide range of credit investments and contingencies, including term loans with prepayment options and grid pricing, dynamic utilization in revolving lines of credit, bonds with put and call options, equities, credit default swaps, retail instruments, commercial real estate loans, and structured instruments.
Traditional approaches to modeling economic capital, credit-VaR, or structured instruments whose underlying collateral is comprised of structured instruments treat structured instruments as a single-name credit instrument i.e., a loan-equivalent). While tractable, the loan-equivalent approach requires appropriate parameterization to achieve a reasonable description of the cross correlation between the structured instrument and the rest of the portfolio. This article provides an overview of how one can calibrate loan-equivalent correlation parameters. Results from taking the approach to the data suggest that structured instruments have far higher correlation parameters than single-name instruments.
In the Moody's KMV Vasicek-Kealhofer (VK) model, asset values and asset returns are calculated separately. Moody's KMV GCorr™ uses weekly asset returns directly from the VK model to calculate asset correlations. As an alternative, asset returns estimated from monthly asset values from Credit Monitor® can be used to estimate asset correlations. This study shows that the asset returns backed out from asset values are vulnerable to capital structure changes and other corporate activities, especially for financial firms. The frequent capital structure changes in financial firms make their correlations from asset values much smaller than the correlations from VK asset returns. Moreover, it is demonstrated that confidence intervals for correlation estimates from three years of monthly returns are much wider than correlation estimates from three years of weekly VK asset returns.
This paper proposes a theoretical framework to account for systematic risk in recovery and to address the correlation between the firm's underlying asset process and recovery. Under the proposed framework, the expected value in default under the risk neutral measure can be expressed as a linear function of the expected value under the physical measure. This allows for a simple mapping between expected recovery observed in the data and a measure that can be applied when using risk neutral valuation methods. When calibrating the model to parameters observed in the data, the risk neutral adjustment results in spreads that are 14% higher for a typical bond, and over 30% higher in some cases. When validating against market data, the evidence suggests that market spreads reflect systematic risk in recovery. We found that approximately 80% of our sample was estimated with a lower absolute error when using the risk neutral adjustment to compute model implied spreads.