Enterprise Risk Modeling

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.

Author: Janet Yinqing Zhao, Douglas W. Dwyer, Jing Zhang
Date: December 12, 2011

Funds transfer pricing (FTP) is the process through which banks allocate earnings to the various lines of business in which they are engaged. The realization that FTP is an important part of enterprise risk mitigation has sparked new interest in this technique, both in regulatorypublications and industry findings. Like any other complex control system, a large body of FTP practices has evolved over time. In this paper, we explore traditional FTP approaches and highlight best practices in FTP methodologies and implementation.

Author: Robert J. Wyle, CFA and Yaakov Tsaig, Ph.D
Date: September 1, 2011

The recent financial crisis has caused risk managers to reevaluate the techniques they use for assessing the risk of extreme losses to their portfolios. Some have argued that the use of distribution-based measures such as VaR and expected shortfall (ES) should be deemphasized in favor of stress-testing and scenario analysis. In this short note we discuss the benefits of stress-testing and scenario analysis. We also describe some limitations of scenario-based approaches as a sole mechanism for assessing portfolio risk.

Author: Roger M. Stein 
Date: June 1, 2011

Read here for an explanation of the differences between through the cycle EDF credit measures and point in time (PIT) measures, and why this distinction is useful. The second part of the slide deck looks at Moody’s Analytics’ Public Firm EDF model and Metrics and how it generates its ratings, along with practical examples of how this can be applied to the individual performance of a firm.

Author: David T. Hamilton
Date: May 3, 2011

How can market signals be interpreted into trends and cycles that offer valuable predictors for future risk/ exposure management? This presentation takes a look at the varying merits of point in time (PIT) and through the cycle (TTC) credit measures, and Moody’s Analytics’ public firm EDF models and metrics which generates the value of Economic Default Frequencies (EDF).

Author: David T. Hamilton
Date: May 3, 2011

Commercial real estate (CRE) exposures represent a large share of credit portfolios for many banks, insurance companies, and asset managers. It is critical that these institutions properly measure and manage the credit risk of these portfolios. 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.

Author: Jun Chen and Jing Zhang
Date: May 3, 2011

There are advantages to measuring credit risk quantitatively, when possible. Nevertheless, qualitative factors may add information, because some credit risk determinants cannot be captured by quantitative measures. We present a framework for producing an internal rating system by overlaying additional factors onto a quantitative model, such as Moody’s Analytics RiskCalc™ EDF™ (Expected Default Frequency).

Authors: Douglas Dwyer, Heather Russell
Date: November 15, 2010

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.

Authors: Nan Chen, Andrew Kaplin, Amnon Levy, Yashan Wang
Date: January 20, 2010

Understanding the relationship between credit and interest rate risk is critical to many applications in finance, from valuation of credit and interest rate-sensitive instruments to risk management. This study empirically examines the relationship between interest rates and default risk using firm level corporate default data in the United States between 1982 and 2008.

Authors: Andrew Kaplin, Amnon Levy, Shisheng Qu, Danni Wang, Yashan Wang, Jing Zhang
Date: October 2, 2009

This document provides a high-level overview of the modeling methodologies implemented in RiskFrontier™ to address the challenges faced by a credit risk manager or a credit portfolio manager. RiskFrontier attempts to accurately model the value of a credit investment at the analysis date and its value distribution at some investment horizon, as well as the correlation between two instruments in a 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, and structured instruments.

Author: Amnon Levy
Date: December 29, 2008