General Information & Client Services
  • Americas: +1.212.553.1653
  • Asia: +852.3551.3077
  • China: +86.10.6319.6580
  • EMEA: +44.20.7772.5454
  • Japan: +81.3.5408.4100
Media Relations
  • New York: +1.212.553.0376
  • London: +44.20.7772.5456
  • Hong Kong: +852.3758.1350
  • Tokyo: +813.5408.4110
  • Sydney: +61.2.9270.8141
  • Mexico City: +001.888.779.5833
  • Buenos Aires: +0800.666.3506
  • São Paulo: +0800.891.2518

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.

Introduction

Credit portfolio risk is measured by the required Economic Capital (EC), which reflects diversification, concentration, and other economic risks. In recent years, however, higher capital standards imposed by new stress testing requirements and Basel III have forced organizations to address how to better manage capital to meet regulatory constraints.

While maintaining the required level of Regulatory Capital (RegC) is necessary and indeed mandatory, simply satisfying the requirement does not necessarily align with stakeholders’ preferences for optimal capital deployment and investment decisions. In other words, RegC and CCAR-style stress testing are requirements that organizations have to adhere to and likely do not reflect how stakeholders trade off risk and return.

For instance, a typical RegC measure, such as the Basel Risk-Weighted Asset (RWA), does not account for diversification and concentration risk, which are important to stakeholders. In general, regulatory measures such as RWA are not as risk-sensitive as economic measures. This shortcoming of RegC underscores the importance of EC, which better captures risks that reflect stakeholders’ preferences.

Ideally, institutions should account for both EC and RegC when making business decisions – including strategic planning, pricing, portfolio management, and performance management. For example, if two potential deals have an identical expected return and RWA but different EC, management should favor the lower EC. Similarly, if two deals have the same EC but different RWA, lower RWA is more desirable.

The challenge lies in quantifying a unifying measure where return, RWA, and EC all enter into a single measure that assesses a deal’s profitability – organizations need a unifying EC and RegC measure. Levy, Kaplin, Meng, and Zhang (2012) propose the concept of integrating EC and RegC. They incorporate regulatory capital requirements into a traditional economic framework underpinning EVA- and RORAC-style decision measures. Xu, Levy, Kaplin, and Meng (2015) provide a practical approach of measuring the degree to which an organization is capital-constrained and the degree to which weight should be placed on RegC in business decisions.

At a high level, RegC should not enter into decision rules when it is not constraining. Organizations do not need to account for the RegC constraint if they meet all RegC requirements regardless of business decisions.

Alternatively, a deal that consumes a high level of RegC is particularly unattractive to an organization that is heavily constrained by RegC.

Xu and Levy (2015) extend the work of Levy, Kaplin, Meng, and Zhang and propose a composite capital allocation measure (mostly referred to as composite capital measure, or CCM) integrating EC and RegC. The metric allocates an institution’s top-of-the-house capital in a way that accounts for both economic risks and the degree to which RegC is constraining. This article provides an overview of these recently developed approaches and discusses how financial institutions can use them to improve risk management and business decisions.

Capital deployment under regulatory capital constraints

The challenge financial institutions face when managing economic and regulatory capital lies in designing and deploying a capital measure that aligns incentives of both management and stakeholders that account for both economic risks and regulatory constraints. While measuring economic risks and RegC on a standalone basis is imperative, a capital charge must ultimately be allocated to align incentives to maximize an organization’s value. The approach proposed by Levy, Kaplin, Meng, and Zhang (2012) and Xu and Levy (2015) highlighted above leverages a traditional economic framework, one where an organization’s stakeholders maximize returns while recognizing risk. The novelty in the approach is in imposing a regulatory constraint. The formal model produces a composite capital measure; whereby the degree to which an organization’s RegC is constraining determines the degree to which weight is placed on RegC.

Historically, the deleverage ratio attributed to Basel and stress testing requirements, defined as the percentage decrease in leverage, is approximately 15% to 30% for US and European banks. This observed deleveraging speaks to the degree to which RegC is constraining.

Figure 1 depicts the relationship between the instrument EC and the required regulatory capitalization rate, also referred to as Risk-Weighted Capital (RWC) (computed by the Basel II standardized approach), on the left side for a typical credit portfolio. In general, RWC is relatively higher for safer instruments, and vice-versa. This finding is also true when RWC is determined according to the Advanced Internal Ratings-Based (IRB) approach, as is shown by Xu, Levy, Meng, and Kaplin (2015) and Xu and Levy (2015).

Figure 1. EC vs. RWC and composite capital measure
EC vs. RWC and composite capital measure
On the left side, instrument RWC plotted against EC. RWC is computed by the Basel II standardized approach. On the right side, instrument CCM plotted against EC. RWC computed by the Basel II standardized approach is used as the input to determine CCM.
Source: Moody's Analytics

The right side of Figure 1 compares instrument CCM with EC. Note that CCM is generally higher than EC. This finding is not surprising, as the regulatory capital constraint is expected to increase the capital needed on top of traditional EC. Another important observation is that two sets of asymptotes exist in this figure. CCM converges with EC as EC increases to a high level. This asymptote reflects CCM’s ability to capture the full spectrum of risk, including diversification and concentration risk unaccounted for by RegC.

As EC decreases, CCM flattens to four levels. Recall, we use the Basel II standardized approach to determine RegC, which results in four unique levels of RWC. Thus, each of the four asymptotes to the left represents the minimum level of capital needed for instruments with a certain RWC level, reflecting CCM’s ability to ensure enough capital is allocated to meet RegC requirements.

Figure 2. EC vs. Effective RWC under CCAR requirements and composite capital measure
EC vs. Effective RWC under CCAR requirements and composite capital measure
On the left side, instrument-effective RWC plotted against EC. Effective RWC computed under the 2015 CCAR severely adverse scenario. On the right side, instrument CCM plotted against EC. CCM computed based on effective RWC under the CCAR severely adverse scenario.
Source: Moody's Analytics

The difference between RegC and EC brings up a dilemma when financial institutions plan capital allocation. On the one hand, the need to meet the ever-increasing regulatory capital standard pulls institutions toward capital allocation by RegC. On the other hand, a sound risk management system calls for a more appropriate capital allocation measure, such as EC, which accounts for not only default risk, but also diversification and concentration risk. The ideal solution leverages a capital allocation measure such as CCM, which takes into account the full spectrum of risk and, at the same time, ensures that the proper amount of capital is allocated to meet regulatory requirements. What is worth highlighting is the tremendous amount of CCM allocated to high credit quality names. While not surprising given the high level of RegC being allocated, the results are striking when compared with EC.

Using RegC-adjusted RORAC, institutions can improve the risk-return attractiveness of the portfolio while meeting RegC requirements ... a 2.5% portfolio turnover rate can increase the expected return of the portfolio by 60 bps, while keeping the required RegC constant. Furthermore, as institutions increase the portfolio turnover rate, the portfolio rate of return on both RegC and EC increases.

Intuitively, CCM can be regarded as a combination of EC and RWC. The relative weight of EC and RWC in CCM is institution-specific. It is determined by how constraining the RegC requirement is for the institution. As Xu, Levy, Meng, and Kaplin (2015) illustrate, the degree of RegC constraint can be measured by how much the institution must deleverage due to the RegC requirement. Historically, the deleverage ratio attributed to Basel and stress testing requirements, defined as the percentage decrease in leverage, is approximately 15% to 30% for US and European banks. This observed deleveraging speaks to the degree to which RegC is constraining.

Figure 3. RegC-adjusted RORAC vs. RORAC
Instrument RegC-adjusted RORAC plotted against unadjusted RORAC under different regulation requirement. On the left, the RegC-adjustment is made under the constraint of the Basel II standardized capital requirement. On the right, the RegC-adjustment is made under the constraint of the CCAR stress testing requirement.
Source: Moody's Analytics

Similar to Basel-style rules, CCAR requires adequate capital under severe economic downturns. This boils down to a required capital buffer that adheres to the portfolio’s RWC, while accounting for erosion due to stressed expected losses conditioned on the downturn scenario. Therefore, the sum of required capital buffer and the stressed expected loss is effectively the minimum capitalization rate institutions need to maintain in order to meet stress testing requirements. We will refer to this sum as the effective RWC.

The left side of Figure 2 compares instrument EC with effective RWC for a sample portfolio under a severely adverse CCAR scenario. As EC decreases, the effective RWC converges to 8%, which is the minimum RegC required. As EC increases, effective RWC becomes much more correlated with EC; instruments with larger EC are associated with more severe losses during a stressed scenario, requiring more capital buffer and a higher effective RWC. Once we know the instrument-effective RWC, we can compute CCM accordingly.

The right side of Figure 2 presents instrument CCM against EC under the CCAR requirement. Similar to CCM under the Basel II capital requirement, instrument CCM under the CCAR requirement also exhibits two asymptotes – CCM converges to EC as EC increases to a high level, and CCM flattens out as EC becomes very small. The intuition behind this pattern is the same as explained previously for CCM under Basel-style capital requirements.

Business decisions under regulatory capital constraints

In practice, stakeholders prefer an institution to deploy capital across the organization and make investment decisions that maximize the institution’s overall return-risk trade-off while satisfying regulatory requirements. Integrating EC with RegC allows financial institutions to allocate capital across businesses with a risk metric that accounts for diversification and concentration risk, as well as the regulatory constraints.

Table 1. Improved portfolio composition using RegC-adjusted RORAC
Source: Moody's Analytics

In addition, the integrated approach provides decision rules that optimize portfolios from an economic perspective while adhering to RegC requirements. Traditional Return on Risk-Adjusted Capital (RORAC) measures are adjusted to account for investments’ RegC burden. Intuitively, the RegC adjustment can be thought of as a tax that lowers an instrument’s effective return.

Figure 3 compares RegC-adjusted RORAC with standard RORAC under Basel II and CCAR. The two measures are generally very different. In particular, safe instruments tend to have very low or even negative RegC-adjusted RORAC; the low return of safe instruments is not sufficient to cover the implicit cost of the RegC constraint.

Using RegC-adjusted RORAC, institutions can improve the risk-return attractiveness of the portfolio while meeting RegC requirements. Table 1 illustrates the impact of re-weighting the sample portfolio where instruments with the lowest RegC-adjusted RORAC are traded for those with the highest RegC-adjusted RORAC. What is impressive is that a 2.5% portfolio turnover rate can increase the expected return of the portfolio by 60 bps, while keeping the required RegC constant. Furthermore, as institutions increase the portfolio turnover rate (i.e., trade more instruments according to RegC-adjusted RORAC), the portfolio rate of return on both RegC and EC increases.

Conclusion

Under higher capital standards imposed by new stress testing requirements and Basel III, organizations should account for both economic risk and regulatory constraints when managing capital and making business decisions. CCM and RegC-adjusted RORAC measures help institutions achieve this goal. CCM allocates an institution’s top-ofthe- house capital in a way that accounts for economic risks, as well as the degree to which RegC is constraining. RegC-Adjusted RORAC helps institutions improve the riskreturn attractiveness of their portfolios, while maintaining the required RegC level.

Sources

1 Moody’s Analytics Quantitative Research Group, Modeling Credit Portfolios, 2013.

2 Amnon Levy, Andrew Kaplin, Qiang Meng, and Jing Zhang, A Unified Decision Measure Incorporating Both Regulatory Capital and Economic Capital, 2012.

3 Pierre Xu, Amnon Levy, Qiang Meng, and Andrew Kaplin, Practical Considerations When Unifying Regulatory and Economic Capital in Investment Decisions, 2015.

4 Pierre Xu and Amnon Levy, A Composite Capital Allocation Measure Integrating Regulatory and Economic Capital, 2015.

SUBJECT MATTER EXPERTS
As Published In:
Related Insights

Project Finance: The Potential Returns

Effective risk assessment approaches to project finance must reflect a true understanding of complex issues. These assessments include the macroeconomic context, which provides an early indication of the potential risks and returns of infrastructure investments.

October 2017 Pdf Dr. Jing Zhang, Kevin Kelhoffer, Jorge A. Chan-Lau

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.

May 2017 Pdf Dr. Amnon Levy, Dr. Pierre Xu

Introduction to CECL Quantification Webinar Slides

In this presentation, our experts Emil Lopez and Jing Zhang, introduce some key CECL quantification methodologies and enhancements that can be made to existing approaches to make them CECL compliant.

February 2017 Pdf Emil LopezDr. Jing Zhang

What Do 20 Million C&I Loan Observations Say about New Origination Dynamics? — Insights from Moody's Analytics CRD Data

We construct and examine new origination of C&I loans to middle-market borrowers using the Loan Accounting System data extracted from Moody's Analytics Credit Research Database (CRD/LAS). We find that C&I loan origination declines during the Great Recession and recovers soon after. The magnitude of the decline and the speed of the recovery varies across segments. For example, new lending to the financial industry decreases more than to the non-financial industry during the recession and recovers faster afterwards. Another example, new originations during the recession consists predominantly of short-term loans, while long-term lending becomes more dominant post crisis. This finding suggests that banks are using loan tenor as a means to mitigate risk during crises, at times even more so than credit quality.

February 2017 Pdf Dr. Pierre Xu, Tomer Yahalom, May Jeng

CECL Webinar Series: Introduction to CECL Quantification

In this presentation, our experts Emil Lopez and Jing Zhang, introduce some key CECL quantification methodologies and enhancements that can be made to existing approaches to make them CECL-compliant.

February 2017 WebPage Emil LopezDr. Jing Zhang

Measuring and Managing Credit Earnings Volatility of a Loan Portfolio Under IFRS 9

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.

January 2017 Pdf Dr. Amnon LevyDr. Yanping PanDr. Yashan Wang, Dr. Pierre Xu, Dr. Jing Zhang, Xuan Liang

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 discuss the strategic impact of IFRS 9 on earnings, capital, and investment concentration. In addition, we discuss how to incorporate these impacts into a strategic business process to better manage the interplay between supply and demand dynamics for regulatory capital.

December 2016 WebPage Burcu Guner, Dr. Amnon Levy

The Degree Regulatory Capital is Constraining and Impact on Investment Decision Rules

Pierre Xu, Associate Director of Portfolio Research at Moody’s Analytics will discuss how required economic capital (EC) accounts for economic risks such as diversification and concentration effects.

October 2016 WebPage Dr. Pierre Xu

Risk Chartis IFRS 9 Market Report

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.

October 2016 Pdf Dr. Amnon Levy, Burcu Guner

Quantitative Research Webinar Series: The Degree Regulatory Capital is Constraining and Impact on Investment Decision Rules

As the techniques and software that underpin Internal Models have matured, the next wave of Internal Model firms can benefit from faster implementations and reduced costs, with off-the-shelf solutions that have been designed to meet the demands of a simulation-based Internal Model.

October 2016 WebPage Dr. Pierre Xu

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.

September 2016 Pdf Dr. Amnon Levy, Dr. Pierre Xu, Dr. Jing Zhang, Andriy Protsyk

Income-Adjusted Risk Contribution-based Capital Allocation

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.

August 2016 Pdf Andrew Kaplin, Dr. Amnon Levy, Mark Wells

Quantitative Research Webinar Series: The Degree to Which Regulatory Capital is Constraining and the Impact on Investment Decision Rules

Prudent risk management needs to account for both the regulatory capital requirement faced by the institution and the intrinsic risk of the portfolio. The Composite Capital Measure helps risk managers to allocate capital and make investment decisions accordingly.

August 2016 WebPage Dr. Pierre Xu

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.

August 2016 WebPage Dr. Amnon Levy

Long-Range Economic Growth: Does Project Finance Matter?

The answer is a definitive “Yes,” suggesting increased project finance investment could become an important tool for addressing sluggish growth concerns brought about by the Great Recession. Empirical results, based on a comprehensive and unique project finance loan database not previously available, show that increasing project finance by one percentage point of GDP could increase real GDP growth per capita by 6 to 10 percent, with growth effects higher for upper-middle income and advanced economies. In other words, in these countries, if GDP per capita is growing at three percent annually, the boost provided by project finance could deliver cumulative, additional growth as high as two percent during the next five years. These results suggest that proposals for stimulating economic growth and productivity via increased project finance merit careful consideration. In contrast, in low-income countries, project finance appears to have less of an impact, possibly owing to deficiencies and weaknesses in financial systems and regulatory frameworks. By addressing these deficiencies, less developed countries could unleash increased growth and productivity.

July 2016 Pdf Dr. Jing Zhang, Kevin Kelhoffer, Jorge A. Chan-Lau

Investment Decisions and Risk-Based Capital Allocation Under Stress Testing Requirements

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.

May 2016 Pdf Dr. Amnon Levy, Dr. Pierre Xu

Using GCorr® Macro for Multi-Period Stress Testing of Credit Portfolios

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.

April 2016 Pdf Noelle Hong, Jimmy Huang, Albert Lee, Dr. Amnon Levy, Marc Mitrovic, Libor Pospisil, Olcay Ozkanoglu

Measuring Required Economic Capital and Parameterizing the Loss Reference Point

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.

March 2016 Pdf Dr. Amnon Levy, Peter Bozsoki, Thomas Tosstorff, Mark Wells

Through-the-Cycle Correlations

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.

January 2016 Pdf Jimmy Huang, Dr. Amnon Levy, Libor Pospisil, Noelle Hong, Devansh Kumar Srivastava

Quantifying Risk Appetite for Limit Setting

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.

November 2015 WebPage Dr. Amnon Levy

Modeling Canadian Commercial Real Estate Loan Credit Risk: An Overview

Commercial real estate (CRE) exposures represent a large market share of credit portfolios for many Canadian banks, credit unions, insurance companies, and asset managers. While this segment escaped the most recent financial crisis relatively safely, Canadian CRE loan portfolios may be facing heightened credit risks given the current changing market conditions, which include sliding oil prices and reduced demand for natural resources and possible interest rate hikes. Given this environment, is critical to use an objective credit risk measurement solution that quantifies CRE loan risks consistently and objectively in order to help assess, stress test, and manage loan portfolios. This paper presents Moody's Analytics Commercial Mortgage Metrics (CMM™) framework, tailored for Canadian CRE loan credit risk, forming the core of our Commercial Mortgage Metrics: Canada (CMM Canada™) product. Based on the well-established CMM U.S. model, our enhanced framework incorporates new factors that capture unique Canada CRE market dynamics and lending practices. We describe our modeling approaches for default probability, loss given default (LGD), Expected Loss (EL), and other related risk measures.

November 2015 Pdf Dr. Jun Chen, Megha Watugala, Dr. Jing Zhang, Tanya Gupta

Practical Considerations When Unifying Regulatory and Economic Capital in Investment Decisions

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.

August 2015 Pdf Dr. Pierre Xu, Dr. Amnon Levy, Qiang Meng, Andrew Kaplin

Quantifying Risk Appetite in Limit Setting

In this paper, we explore leveraging an organization's economic capital framework to quantify the RAS via risk- and macro scenario-based limits.

June 2015 Pdf Andrew Kaplin, Dr. Amnon Levy, Qiang Meng, Libor Pospisil

Unifying Regulatory and Economic Capital for Investment Decisions

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.

May 2015 WebPage Dr. Amnon Levy

RiskCalc Plus C&I Stress Testing PD & LGD Model (granular approach) Overview

To help our clients build benchmark commercial and industrial (C&I) loss models for the Federal Reserve's Comprehensive Capital Analysis and Review (“CCAR”)/DFAST exercises, we have developed an approach designed specifically to calculate provisions for losses of C&I portfolios. Our approach utilizes Moody's Analytics probability of default (PD), loss given default (LGD), and exposure at default (EAD) econometric models, which are intuitive, parsimonious, make economic sense, and have good statistical fit. We construct these models using our public EDF credit measures, RiskCalc™ private firm EDF credit measures, and Moody's Default & Recovery Database and Credit Research Database.

July 2014 Pdf Nan Chen, Jian Du, Heather Russell, Dr. Douglas DwyerDr. Jing Zhang, Zhong Zhuang

Usage and Exposures at Default of Corporate Credit Lines: An Empirical Study

A major source of firm funding and liquidity, credit lines can pose significant credit risk to the underwriting banks. Using a unique dataset pooled from multiple U.S. financial institutions, we empirically study the credit line usage of middle market corporate borrowers. We examine to what extent borrowers draw down their credit lines and the characteristics of those firms with high usage. We study how line usage changes with banks' internal ratings, collateral, and commitment size and through various economic cycles. We find that defaulted borrowers draw down more of their lines than non-defaulted borrowers. They also increase their usage when approaching default. Risky borrowers tend to utilize a higher percentage of their credit lines as well.

CCAR and Beyond: Capital Assessment, Stress Testing, and Applications – An Introduction

Comprehensive Capital Analysis and Review (CCAR) has quickly become one of the most dominant regulatory regimes for US banks in recent years. Of the new regulatory requirements BHCs must address, CCAR is widely considered to have the greatest influence on banks' risk management and business practices. Against the backdrop of CCAR's profound impact, there have been few, if any, systematic treatments of the subject. CCAR and Beyond: Capital Assessment, Stress Testing and Applications, a new book published by Risk Books, is designed as a unique source of information and insight from key figures involved in CCAR. This book, with fifteen contributed chapters, represents a timely, concerted and collective effort to provide comprehensive and authoritative coverage of CCAR and its implications.

February 2014 Pdf Dr. Jing Zhang

Quantitative PPNR Modeling

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.

January 2014 Pdf Dr. Amnon Levy

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.

October 2013 WebPage Thomas Day, Dr. Amnon Levy, Robert Wyle

Joint Modeling of Conditional Credit Migration and Default: New Answers to Old Problems

In this presentation, Dr. Jing Zhang, Divisional Managing Director and Global Head of Quantitative Research at Moody's Analytics, shares new solutions to joint modeling of condition credit migration and default that are lightweight, simple to implement, and complementary to your bank's internal data and approach.

October 2013 Pdf Dr. Jing Zhang

Integrating Economic Capital, Regulatory Capital and Regulatory Stress Testing

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.

October 2013 Pdf Dr. Amnon Levy

A New Approach to Accounting for Regulatory and Economic Capital

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.

August 2013 WebPage Dr. Amnon Levy

A New Approach to Accounting for Regulatory and Economic Capital

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.

August 2013 Pdf Dr. Amnon Levy

A Unified Approach to Accounting for Regulatory and Economic Capital

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.

August 2013 Pdf Dr. Amnon Levy, Andrew Kaplin, Qiang Meng, Dr. Jing Zhang

An Overview of Modeling Credit Portfolios

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.

June 2013 Pdf Dr. Amnon Levy

Applications of GCorr™ Macro: Risk Integration, Stress Testing, and Reverse Stress Testing

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.

April 2013 Pdf Mariano Lanfranconi, Libor Pospisil, Andrew Kaplin, Dr. Amnon LevyNihil Patel

A Unified Decision Measure Incorporating Both Regulatory Capital and Economic Capital

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.

January 2013 Pdf Dr. Amnon Levy, Andrew Kaplin, Qiang Meng, Dr. Jing Zhang

CCAR Stress Testing and Its Implications for Risk Modeling

CCAR Stress Testing and Its Implications for Risk Modeling

October 2012 WebPage Dr. Jing Zhang

New Risk Management Techniques that Improve Strategic Planning

New Risk Management Techniques that Improve Strategic Planning

October 2012 WebPage Dr. Amnon Levy, Randy Miller

A Model-Based Approach to Constructing Corporate Bond Portfolios

In this whitepaper, we discuss our model-based approach for constructing investment grade and high yield corporate bond portfolios that consistently beat representative market benchmarks.

April 2012 Pdf Dr. Jing Zhang, Zan Li

Modeling Commercial Real Estate Loan Credit Risk: An Overview

In this paper, we present the Moody's Analytics framework for measuring commercial real estate loan credit risk, which is the model at the core of our Commercial Mortgage Metrics (CMM)™ product. We describe our modeling approaches for default probability, loss given default (LGD), Expected Loss (EL), and other related risk measures.

May 2011 Pdf Dr. Jun ChenDr. Jing Zhang

Quantifying the Value of Implicit Government Guarantees for Large Financial Institutions

This methodology takes a hard look at "too-big-to-fail" financials and the market value of the government's guarantee to them.

January 2011 Pdf Zan Li, Shisheng Qu, Dr. Jing Zhang

Moody's Analytics RPC Presentation: Levy Wang on Assessing and Pricing Liquidity Risk

Moody's Analytics RPC Presentation: Levy Wang on Assessing and Pricing Liquidity Risk

January 2011 Pdf Dr. Amnon LevyDr. Yashan Wang

Bulletproofing an Index-Benchmarked Portfolio

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.

November 2010 Pdf Dr. Amnon Levy

Analyzing the Impact of Credit Migration in a Portfolio Setting

(This version was written for publication in the Encyclopedia of Quantitative Finance, John Wiley & Sons LTD Publishing) 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.

September 2010 Pdf Yaakov Tsaig, Dr. Amnon LevyDr. Yashan Wang

Investing in Corporate Credit Using Quantitative Tools

Corporate credit is an important investment class with potentially attractive returns. The asymmetric nature of corporate bond returns implies that investing in credit is not about picking “winners,” but rather about avoiding “losers” (i.e., defaults and spreads blow-ups). Investors can utilize quantitative measures of credit risk to minimize the risk of such events.

September 2010 Pdf Zan Li, Dr. Jing Zhang

Navigating Through Crisis: Validating RiskFrontier® Using Portfolio Selection

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.

April 2010 Pdf Zhenya Hu, Dr. Amnon LevyDr. Jing Zhang

CDS-implied EDF™ Credit Measures and Fair-Value Spreads

In this paper, we present a framework that links two commonly used risk metrics: default probabilities and credit spreads. This framework provides credit default swap-implied (CDS-implied) EDF™ (Expected Default Frequency) credit measures that can be compared directly with equity-based EDF credit measures.

March 2010 Pdf Dr. Douglas Dwyer, Zan Li, Shisheng Qu, Heather Russell, Dr. Jing Zhang

Implications of PD-LGD Correlation in a Portfolio Setting

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.

February 2010 Pdf Qiang Meng, Dr. Amnon Levy, Andrew Kaplin, Dr. Yashan Wang, Zhenya Hu

Risk Integration: New Top-down Approaches and Correlation Calibration

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.

January 2010 Pdf Nan Chen, Andrew Kaplin, Dr. Amnon LevyDr. Yashan Wang

The Relationship Between Average Asset Correlation and Default Probability

Asset correlation and default probability are critical drivers in modeling portfolio credit risk. It is generally assumed, as in the Basel II Accord, that average asset correlation decreases with default probability. We examine the empirical validity of this assumption in this paper. Overall, we find little empirical support for this decreasing relationship in the data for corporate, commercial real estate (CRE), and retail exposures. For corporate exposures, there is no strong decreasing relationship between average asset correlation and default probability when firm size is properly accounted for. For CRE and retail exposures, the empirical evidence suggests that the relationship is more likely to be an increasing one.

July 2009 Pdf Joseph Lee, Joy Wang, Dr. Jing Zhang

Understanding Asset Correlation Dynamics for Stress Testing

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.

July 2009 Pdf Qibin Cai, Dr. Amnon LevyNihil Patel

An Overview of Modeling Credit Portfolios

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.

December 2008 Pdf Dr. Amnon Levy

Modeling Correlation of Structured Instruments in a Portfolio Setting

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.

November 2008 Pdf Tomer Yahalom, Dr. Amnon Levy, Andrew Kaplin

Asset Correlation, Realized Default Correlation, and Portfolio Credit Risk

Asset correlation is a critical driver in modeling portfolio credit risk. Despite its importance, there have been few studies on the empirical relationship between asset correlation and subsequently realized default correlation, and portfolio credit risk. This three three-way relationship is the focus of our study using U.S. public firm default data from 1981 to 2006. We find the magnitude of default-implied asset correlations is significantly higher than has been reported by other studies. There is a reasonably good agreement between our default-implied asset correlations and the asset correlation parameters in the Basel II Accord for large corporate borrowers. However, the recommended small size adjustment in the Basel II Accord still produces asset correlation higher than what we observe in our data. More importantly, we find that measuring asset correlation ex ante accurately can improve the measurement of subsequently realized default correlation and portfolio credit risk, in both statistical and economic terms. These results have several important practical implications for the calculation of economic and regulatory capital, and for pricing portfolio credit risk. Furthermore, the empirical framework that we developed in this paper can serve as a model validation framework for asset correlation models in measuring portfolio credit risk.

March 2008 Pdf Dr. Jing Zhang, Fanlin Zhu, Joseph Lee

Using Asset Values and Asset Returns for Estimating Correlations

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.

September 2007 Pdf Brian Dvorak, Fanlin Zhu, Dr. Jing ZhangDr. Amnon Levy

Incorporating Systemic Risk In Recovery: Theory and Evidence

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.

April 2007 Pdf Dr. Amnon Levy, Zhenya Hu

An Empirical Assessment of Asset Correlation Models

Rigorously validating and monitoring the out-of-sample performance of correlation models is vital to their acceptance and successful implementation in practice. In this paper, by applying both statistical and economic criteria, we validate the out-of-sample performance of a number of asset correlation models using data on more than 27,000 firms worldwide. Our results show that well-constructed correlation models can produce reasonably accurate estimates of future correlations, thereby leading to more optimal portfolio construction and more accurate measurement of risk contributions of individual assets. The results also show that the widely used one factor models and Industry Average Model perform worse than suggested in the financial literature. Furthermore, we show that country effects play an important role in determining correlation structure. These results have significantly extended the current understanding of correlation models.

November 2001 Pdf Bin Zeng, Dr. Jing Zhang