Our models accurately calculate stand-alone risk and return measures of instruments in a portfolio, as well as portfolio-referent risk of those instruments. Our models combine these measures to deliver an extensive set of economic performance metrics, including portfolio return, expected loss, unexpected loss, economic capital, and expected shortfall. Our portfolio framework supports a wide range of credit investments and contingencies, including bonds, equities, term loans, revolving credit lines, credit derivatives, and structured instruments.
Financial institutions are seeking ways to gain a better understanding of their credit portfolios’ risk dynamics, allowing them to foresee and to prepare for potential increases in capital requirements resulting from economic shocks.
Aubrey Clayton, Associate Director, and Xuan Liang, Assistant Director of Portfolio Research at Moody’s Analytics, will discuss how to leverage a multi-period capital planning framework to determine the appropriate capital buffer level for a portfolio under various economic scenarios and how to fortify capital buffers through portfolio selection across periods of macroeconomic stress.
Overview of Multi-Period Capital Analysis
Brute Force Approach
Proxy Function Approach
Presenters: Aubrey Clayton, Xuan Liang
Date: October 2016
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.
Authors: Peter Bozsoki, Amnon Levy, Thomas Tosstorff, Mark Wells
Date: March 7, 2016
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.
Presenter: Jimmy Huang, Dr. Amnon Levy, Libor Pospisil, Noelle Hong, Devansh Kumar Srivastava
Date: January 26, 2016
In this webinar, Moody's Analytics provides an overview of a number of approaches and best practices for setting risk- and macro scenario-based limits to quantify a risk appetite statement.
Presenter: Dr. Amnon Levy
Date: November 3, 2015
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.
Authors: Pierre Xu, Amnon Levy, Qiang Meng, Andrew Kaplin
Date: August 4, 2015
In this paper, we propose a composite capital allocation measure integrating regulatory and economic capital, based on the model introduced by Levy, Kaplin, Meng, and Zhang (2012). We show that our measure has ideal properties of an integrated capital measure. It specifies the capitalization rate needed to achieve the desired deleverage ratio — when the regulatory capital constraint is binding, composite capital for the overall portfolio aggregates to an institution’s top-of-the-house target capitalization rate. Our measure also redistributes required capital across instruments to account for instrument heterogeneities in both economic capital and the impact of regulatory requirements and, thus, has a natural application toward capital allocation. We find this measure is significantly higher than economic capital for instruments with high credit quality, reflecting the fact that the regulatory capital requirement is relatively more onerous for these instruments.
Authors: Pierre Xu, Amnon Levy
Date: August 6, 2015
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. Since the financial crisis, regulations have forced financial institutions to adhere to increasing capital standards. New stress testing requirements and the advent of Basel III have raised the need for better capital management. A central problem faced by institutions today is to understand how to incorporate the impact of regulatory capital requirements into traditional economic capital-styled risk management.
Presenter: Amnon Levy
Date: July 21, 2015
Moody’s Analytics GCorr™ Corporate model provides asset correlations of corporate borrowers for credit portfolio analysis. The GCorr Corporate model is based on 49 country factors. This paper introduces a new model, GCorr Emerging Markets, designed with more than 200 country-factors including emerging markets worldwide. The methodology expands GCorr Corporate’s 49 country factors to 200+ factors, each representing individual countries to better measure country concentration and diversification effects. The expanded factors cover predominately emerging market countries where we lack firm-level asset return data. For this reason, we refer to the extension as the GCorr Emerging Markets model. This model allows financial institutions with commercial exposures to smaller and emerging countries to better describe correlations across these countries, as well as to better capture diversification effects when investing in a wide cross-section of these countries.
Authors: Jimmy Huang, Libor Pospisil, Noelle Hong
Date: July 13, 2015
This document details the analysis we perform to better understand SMEs’ credit quality dynamics and to quantify the differences in credit migration between SMEs and public firms. We estimate a set of transition matrices for SMEs based on Moody’s Analytics Credit Research Database (CRD™), a comprehensive dataset containing loan and accounting information for a large sample of SME borrowers. We then validate these transition matrices to show their stability across different sizes and industry segments, as well as across geographies. Our analytical comparison of transition matrices highlights similarities in migration dynamics across different SME segments.
Authors: Peter Bozsoki, Sunny Kanugo, Yashan Wang
This document presents validation results for the credit-interest lattice or the multi-dimensional lattice (MDL) valuation model within Moody’s Analytics RiskFrontier™. We focus on valuations of a large sample of corporate bonds, January 2006 – July 2013. We also produce valuations using the credit-only lattice and compare performance of the two lattice models. We find that model valuations compare extremely well with market transaction prices. Further, the MDL model produces better valuations for high credit quality bonds, especially in higher interest rate regimes. These findings validate the model’s ability to accurately value risky assets while accounting for both credit and interest rate risks. We also compare the bottom-up approach for risk integration implemented in the joint lattice model with a traditional top-down approach.
Authors: Sunny Kanugo, Yanping Pan, Rama Sankisa, Yashan Wang
Date: June 18, 2015
In this webinar, Moody’s Analytics will discuss risk-based limits and macro scenario-based limits, both of which can be used to quantify a risk appetite statement. The risk appetite statement of an institution specifies the aggregate level and types of risks the institution is willing to take or avoid in order to achieve its business goals. The translation of risk appetite into limits allows an organization to achieve its strategic objectives and business plan while adhering to its risk capacity.
Presenter: Amnon Levy
Date: June 5, 2015