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

Opacity – rooted in fragmented markets, complex legal entity structures, and contingent liabilities – has prompted regulators to require more specific insight from certain financial firms into their interconnections. In this article, we highlight a new network-based toolkit that helps firms deal with associated regulatory requirements related to single-counterparty credit limits.


In the first quarter of 2016, the Federal Reserve released a revised proposal that would establish credit limits for unaffiliated counterparties of large financial institutions.1 The proposal – full of intricacies related to control relationships for economically interdependent counterparties – strives to provide additional transparency to the network of financial connections between large firms. Further details of the proposal are described in Figure 1.

While the proposed rule is a step toward improved financial market transparency, it has at least two weaknesses that require attention. First, it instructs financial firms to aggregate counterparties based on the likelihood that he distress of one could interrupt the other’s payment of liabilities, but does not specify a clear way to assess such relationships. Second, a bank’s ability to assess connections between two of its own counterparties does not necessarily allow it to detect problems arising from a third counterparty linking these two.

We propose a tool that allows banks subject to single-counterparty credit limit (SCCL) rules to accomplish both of these objectives, and therefore to satisfy the SCCL requirements for economically interdependent counterparties in a more concrete manner.

SCCL Redux

If enacted in their current form, the new SCCL requirements will require large financial institutions in the US, both domestic and foreign, to track and report their net credit exposures to unaffiliated counterparties based on many specific criteria. As has been the case for many recent expectations from the Federal Reserve, the proposal tailors supervisory expectations based on the size of the firm as well as its systemic footprint. Limits for allowable exposures, as well as frequency of reporting, are tiered in a manner similar to the revised US Basel III capital rules.2 Figure 2 outlines capital limits and reporting requirements for each tier of bank holding company (BHC).

Figure 1. Counterparty relationships

Affiliated counterparties outlined in the proposal are generally aligned with control relationships, following the Fed’s definition of a subsidiary outlined in the Bank Holding Company Act of 1956.

The Fed’s definition of economically interdependent counterparties is a bit more convoluted and is based on six criteria outlined in the proposal. Generally speaking, the criteria have been established to track counterparties that are likely to move in tandem due to various economic and legal relationships between the counterparties.

For the purposes of this article, we focus on the most open-ended of the six requirements: financial distress spillover. This is the likelihood that the financial distress of one counterparty will cause difficulties for another counterparty in terms of full and timely payment of liabilities.

Source: Federal Reserve

The call for consistent data collection on a firm’s large counterparties is appropriate. Without additional analytics, however, risk managers will not be able to take advantage of actionable risk information as a result of such efforts. By design, limiting the analysis to assessing direct, "one step" connections ignores a large majority of the credit risk pathways linking these large firms. We now describe briefly a network-based toolkit that solves this problem.

Figure 2. SCCL capital and reporting requirements
SCCL capital and reporting requirements
Source: Federal Reserve

Counterparty Risk Identification with Dynamic Network Models

A bank’s hedged and unhedged counterparty exposures provide direct measures for the potential for losses due to a counterparty default.

But indirect exposure, caused by the impacts of major counterparties on each other, can also play a significant role in raising a bank’s credit risk. For this reason, banks must take counterparty contagion risk into account in the risk identification process. In particular, risk identification should capture the observed effects of counterparty probabilities of default (PD) on the bank’s and other counterparties’ PDs over recent history. This is a direct way of satisfying the “financial distress spillover” requirement of SCCL regulation discussed in Figure 1.

As a concrete illustration of these ideas, we present a short case study based on a large global bank. We use estimates of a dynamic network model3 to measure the existence and strength of spillovers across counterparty PDs based on monthly data taken from the last five years. The underlying concept behind the dynamic network model is that of Granger causality. This is assessed based on the results of running a vector autoregression (VAR) using the PDs of each distinct pair of financial firms in the network. The particular network we use for this analysis is the global megabanks network, which includes all publicly traded financial firms worldwide with at least $100 billion in assets on their books.

The network model sources PD estimates from Moody’s Analytics CreditEdge, which provides default probability estimates for publicly traded firms around the world. The specific metric it uses to measure a firm’s PD is the one-year Expected Default Frequency (EDF™). EDF metrics are estimated using combined information on firm balance sheets, the market value of traded equity, and a large history of observed default events. In the dynamic network methodology, firms whose EDF movements forecast movements in the EDFs of many other firms are recorded as being highly connected and having an outsized influence on systemic risk.

Counterparty risk analysis that ignores these secondary effects is likely to miss materially important channels for contagion of counterparty credit risk to a bank’s PD.

Analysis of a Case Study for a Large Global Bank

To illustrate the application of the network model to the “financial distress spillover” aggregation rule, we discuss briefly the case of an unnamed large global bank, which we will refer to as the “test bank.” Note that the analysis that follows would proceed in exactly the same manner for any other bank in the network.

Our analysis relies on an anonymized list of 22 counterparties for the test bank, which was determined based on its largest direct counterparty exposures. Because we already know which counterparties the bank has direct exposure to, our interest is in comparing this information against the sensitivities measured by the dynamic network model and ranking the counterparties in order of the test bank’s sensitivity to their PDs.

Our findings are as follows. First, of the 22 direct test bank counterparties, the measured predictive ability of one counterparty in particular – let us call this “counterparty V” – was much greater than for all the rest with respect to the EDF of the test bank. The sum of the first and second monthly lag coefficients in the VAR(2) models involving our test bank and each of its major counterparties, respectively, line up closely with the results of the F-test used to assess which banks Granger-cause the test bank’s EDF. Figure 3 displays a bar chart of the values of these sums by (anonymized) counterparty, with counterparties arranged by increasing order of the sum. Somewhat surprisingly, we find that only bank V Granger-causes the test bank’s PD (based on the 5% significance threshold for the relevant F-test), although the sum of coefficients is positive for all but three of the counterparties. By computing this same (or similar) measure for any bank in the network with respect to all of the other firms, it is straightforward to satisfy the Fed’s “financial distress spillover” rule: Simply aggregate counterparties with positive spillover strength over an appropriate threshold.

Figure 3. Strength of credit risk spillover for each direct counterparty bank
Strength of credit risk spillover for each direct counterparty bank
Source: Moody's Analytics CreditEdge

Our second finding relates to validation. One simple way to validate the network model for the purposes of counterparty risk identification is to rank each of the financial institutions in the network based upon the combined strength that the credit risk of the test bank’s 22 counterparties exerts upon them. If the test bank ranks highly in this list, this verifies that its list of major direct counterparty exposures strongly influences its credit risk. Upon performing this exercise for the network of global megabanks, we find that our test bank falls at the 80th percentile according to this measure for the list of 22 counterparties. This demonstrates that our test bank is high on the list of large financial firms whose credit risk is driven by the credit risk of its largest counterparties. Figure 4 illustrates this result.

Figure 4. Strength of counterparty group effect
Strength of counterparty group effect
Source: Moody's Analytics CreditEdge

Finally, on the issue of third-party risk transfer, much can be learned about potential knock-on effects from a counterparty default by looking at which of the test bank’s counterparties Granger-caused each other and ranking which of these contagion channels are strongest.

When we do this exercise for the test bank, we find that all but one of its counterparties are Granger-caused by at least one other bank in its counterparty list. Further, of the 21 counterparty banks in the latter category, 18 are driven by at least two other counterparties. The strength of many of these relationships is quite high – even stronger than the effect of the test bank’s most important counterparty on its own PD! Counterparty risk analysis that ignores these secondary effects is likely to miss materially important channels for contagion of counterparty credit risk to a bank’s PD.


The systemic and counterparty risk surveillance tools provided by dynamic network models can be productively used to shed light on which of a bank’s known counterparties most influence its credit risk based on measured relationships from recent history. Counterparties can be grouped based on the strength of their relationships with one another. Further information about the methodologies used here can be found in Hughes and Malone (2015) and Malone (2015, 2016). For regulatory purposes, the strength of the spillover effects between counterparties can be used to group them in order to satisfy SCCL regulation.


1 Board of Governors of the Federal Reserve System, 2016.

2 Regulatory Capital Rules: Regulatory Capital, Implementation of Basel III, Capital Adequacy, Transition Provisions, Prompt Corrective Action, Standardized Approach for Risk weighted Assets, Market Discipline and Disclosure Requirements, Advanced Approaches Risk-Based Capital Rule, and Market Risk Capital Rule.

3 This is documented in Hughes and Malone (2015) and Malone (2015, 2016).


Board of Governors of the Federal Reserve System. “Calibrating the Single-Counterparty Credit Limit between Systemically Important Financial Institutions.” March 2, 2016.

Hughes, Anthony and Samuel W. Malone. “Systemic Risk Monitor 1.0: A Network Approach.” Moody’s Analytics whitepaper. June 2015.

Malone, Samuel W. “Credit Risk Cascades: Forecasting PDs Under Compound Scenarios.” Moody’s Analytics whitepaper. November 2015.

Malone, Samuel W. “Solving the Counterparty Default Scenario Problem: A 2016 CCAR Case Study.” Moody’s Analytics Risk Perspectives. June 2016.

As Published In:
Related Insights

Modeling Stressed LGDs for Macroeconomic Scenarios

In this article, we model stressed LGDs as a function of macroeconomic drivers and find that LGDs sometimes lead PDs by several months during crisis periods.

Brexit Fallout: Using Scenario Analysis and a Systemic Risk Approach to Assess Corporate Credit Risk

The June 23rd referendum, in which UK voters chose to leave the European Union, has fanned financial volatility and may precipitate a recession in the UK economy. The updated economic and financial outlook has implications for corporate credit risk.

August 2016 WebPage Glenn Levine, Danielle Ferry, Dr. Samuel W. Malone

DFAST Quick Takes: Looking Toward CCAR Results

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.

June 2016 WebPage Anna KraynDavid LittleEd Young

Reading the Tea Leaves of Recent Regulatory Guidance

In this article, we review the common themes reflected in recent regulatory guidelines released by the Federal Reserve and the BCBS.

June 2016 WebPage Anna KraynDavid LittleEd Young

Solving the Counterparty Default Scenario Problem

This article introduces Credit Risk Cascades, a new model that forecasts probability of default of financial institutions under compound scenarios. The model seamlessly integrates macroeconomic, counterparty, and systemic risk projections.

June 2016 WebPage Dr. Samuel W. Malone

Benefits & Applications: AutoCycle - Vehicle Residual Value Forecasting Solution

With auto leasing close to record highs, the need for accurate and transparent used-car price forecasts is paramount. Concerns about the effect of off-lease volume on prices have recently peaked, and those exposed to risks associated with vehicle valuations are seeking new forms of intelligence. With these forces in mind, Moody's Analytics AutoCycle™ has been developed to address these evolving market dynamics.

May 2016 Pdf Dr. Tony HughesDr. Samuel W. Malone, Michael Vogan, Michael Brisson

Making Risk Appetite Stick: How Data and Analytics Can Help

In this webinar, we discuss how institutions can overcome challenges to ensure that risk appetite can be monitored as well as key analytic metrics which can be leveraged for strategic decision-making.

April 2016 WebPage Nihil PatelEd Young

Reading the Tea Leaves of Recent Regulatory Guidance

December 2015 was a busy month for regulatory agencies and global standard setters. Throughout the year the industry has been waiting for additional guidance on high impact topics including capital planning and allowance methodologies, and in the final stretch of 2015 both the Federal Reserve and the Basel Committee on Banking Supervision (BCBS) complied. This paper will primarily focus on common themes in the two releases.

January 2016 Pdf Anna KraynEd YoungDavid Little

Using a Risk Appetite Framework to Align Strategy and Risk

In this article, we provide an overview of some common problems organizations face and introduce a solution to develop an integrated, transparent, measurable, and actionable Risk Appetite Framework.

December 2015 WebPage Anna KraynEd Young

Systemic Risk Monitor 1.0: A Network Approach

In this article, we introduce a new risk management tool focused on network connectivity between financial institutions.

Measuring Systemic Risk in the SE Asia Financial System

In this webinar, Moody’s Analytics combines the techniques of network analysis with the richness of Moody’s CreditEdge™ platform to compute systemic risk measures spanning the last 20 years for five major southeast Asian economies.

June 2015 WebPage David HamiltonDr. Samuel W. Malone

Measuring Systemic Risk in the Southeast Asian Financial System

This article looks back at the Asian financial crisis of 1997-1998 and applies new methods of measuring systemic risk and pinpointing weaknesses, which can be used by today’s financial institutions and regulators.

Learnings from CCAR 2015 and Beyond

In this webinar, Moody's Analytics experts revisit the CCAR 2015 scenarios, review industry results and discuss how to identify and quantify Systemic Risk.

April 2015 WebPage Mark Zandi, Anna KraynDr. Samuel W. Malone