30
Oct

Estimating Default Risk in Fund Structures

We recently hosted a series of Knowledge Management sessions titled “Spark” on an array of topics. Over the coming days we will be sharing notes and proceeds from these sessions. We begin with proceeds from Ravi Saraogi’s session on “Estimating Default Risk in Fund Structures” where he talked about why such estimation is a challenging task and how this process has evolved over the years and the current methodologies for assessing such risks.

By Ravi Saraogi

Estimating default risk in Fund structures can be challenging. One way to assess the credit rating of a Fund structure or a Collateralized Debt Obligation (CDO) is to use the weighted-average rating (WAR) of the underlying asset pool. However, such a measure does not sufficiently describe credit quality. Two portfolios with the same WAR could have very different risk and return characteristics. A portfolio with only BB rated securities compared to a portfolio with some AA and many CCC rated securities can have the same WAR, but will have very different volatility indicators.

Several techniques have been used to assess credit risk in Fund structures and CDOs. Unlike a bond, whose stand-alone credit rating is sufficient to communicate riskiness, the credit quality of a collection of bonds under a Fund structure cannot be easily imputed. Below, we describe the prominent rating methodologies for such structures.

Moody’s Binomial Expansion Technique

Moody’s Binomial Expansion Technique (BET) is one of the earliest known techniques of credit rating. Introduced in 1996, it continues to be used in Fund and Collateralized Debt Obligations (CDOs) analysis alongside other methods. Under this method, a hypothetical portfolio of uncorrelated and homogenous assets is created from the original asset pool. The number of assets in this hypothetical subset portfolio is termed as the ‘diversity score’ (DS). Given homogeneity and the non-correlation in the hypothetical portfolio, DS+1 default situations are captured and the probability of each default scenario occurring is derived through a binomial formula. The expected loss (EL) is then calculated as the product of default probability and the loss severity in the event of a default. Based on the DS+1 default simulations, the loss distribution curve is derived which is compared to a target loss distribution to check if the credit enhancement is adequate1.

There are a number of limitations associated with the BET model, the most significant being its restrictive assumptions. The assumption of zero correlation in the constructed hypothetical portfolio means that the EL will be accurately calculated only when there is, in fact, no correlation in the asset pool. This means BET fails the accuracy test due to the presence of correlation in the larger asset pool. Indeed, empirical tests show that ‘BET underestimates EL when the collateral assets are correlated, with the degree of underestimation rising in the subordination level’2. Another drawback of BET is that it uses discrete probability distribution as compared to continuous distribution in the Monte Carlo (MC) simulation approach.

Standard & Poor’s Monte Carlo Simulation Technique

The difficulty of deriving a credit rating for a Fund structure or a CDO from the credit rating of the underlying portfolio, either in terms of the applicability of WAR or BET or mathematical imputations, has led to the widespread adoption of simulation in CDO credit ratings3.

S&Ps rating approach to CDOs can be broken into two steps. In the first step, an expected loss distribution for the underlying assets is estimated. In the second step, cash flow simulations are conducted to check if a particular tranche can withstand the required level of defaults for a given rating.

The expected loss distribution is estimated using MC simulations based on the historically observed Cumulative Default Rate (CDRs) for the underlying rated assets and correlation assumptions. Post the derivation of the expected loss distribution, two statistics are computed by S&P – the ‘scenario default rate’ (SDR) and the ‘breakeven default rate’ (BEDR). The SDR is defined as the extent of default in the underlying asset pool that a tranche must be able to withstand to secure the rating assigned to it, while the BEDR indicates the actual level of default in the underlying asset pool which a tranche sustains based on cash flow simulations4.

Next, the cash flow simulations are run in the underwriter’s cash flow model. If the BEDR is more than (or equal) to the SDR, the credit enhancement for that tranche is higher (or equal) than what is required for assigning that rating to the pool.

Imputing Credit Rating Through Default Instances

An intuitive way to assess credit risk in Fund structures is to use MC simulation to compute the number of default instances and compare the same to historical CDRs. This can be done by taking the cash flow outputs from the underwriter’s model and running it past the Fund or CDO payment waterfall. For each such simulation, a counter can be built to record if cash flows were sufficient to meet payment commitments or not. At the end of the consolidated simulation, the default rate can be mapped to the historical corporate bond default rate matrix for imputing the credit rating.

The above provides a brief introduction on the different approaches for estimating default risk in Fund structures and the same has been discussed in detail in the presentation. These methodologies have witnessed increased scrutiny post the US subprime crisis when several highly rated Fund and CDO structures went bust5,6. The crisis highlighted the need to better understand risk when assets are pooled under different investment structures; distinct from evaluating risk in a single financial security.

View the presentation below:

View the video from the session below:


  1. Garcia, J., et al. (2004), On Rating Cash Flow CDO’s using BET technique. http://www.geocities.ws/joaogarcia18/DexiaCreditMethodology/CDOBETPaper60Web.pdf
  2. Fender, I., Kiff, J., 2004. CDO rating methodology: Some thoughts on model risk and its implications. Bank of International Settlements Working Papers No.163
  3. Myers, S., 1976. Postscript: Using Simulation for Risk Analysis- Modern Developments in Financial Management. Praeger Publications. pp. 457–463.
  4. Standard & Poor’s. 2002. Global Cash Flow and Synthetic CDO Criteria. S&P Structured Finance.
  5. Benmelech, E., Dlugosz, J., 2009. The alchemy of CDO credit ratings. Journal of Monetary Economics.56, 617-634.
  6. Griffen, J.M., Tang, D.Y., 2011. Did Subjectivity Play a Role in CDO Credit Ratings? Journal of Finance. Volume 67, Issue 4, pages 1293–1328.

24
Oct

NSE-IFF Financial Deepening & Household Finance Research Initiative (2014-15) – Global Call for Proposals

The National Stock Exchange, India and IFMR Finance Foundation are delighted to announce a joint call for research proposals, under the “NSE-IFF Financial Deepening and Household Finance Research Initiative” for the years 2014-15. The initiative is looking to fund several high-quality research studies that can be of benefit to policy makers in the design of financial sector policy and to practitioners as they seek to design products and services for low-income and excluded populations.

This year, we invite research proposals under the broad themes of financial inclusion, financial deepening and household finance. Interested researchers affiliated with universities, think-tanks and other institutions (both Indian and overseas), and students enrolled in Doctoral programs are welcome to apply.

For a description of the research themes, application deadlines and other details and modalities, please visit: http://bit.ly/nse-iff-research2014

6
Oct

Barriers to Basic Banking: Results from an Audit Study

In new study from NSE and IFMR, researchers use a “mystery shopping” approach where trained auditors, posing as low-income customers, attempt to open BSBDA (Basic Savings Bank Deposit Account) and low-cost accounts at banks in Chennai. The researchers rigorously document the barriers faced by potential customers, including complete recording of all interactions between auditors and bank staff. Their study finds that bank staff almost always refuse to offer the BSBDA, and aggressively discourage customers from opening other low-cost accounts. The researchers suggest immediate intervention to monitor ongoing financial inclusion policy implementation, and caution against driving G2P transfers until banks demonstrate capacity and willingness to meet basic standards of service and client protection.

From the abstract:

“We conducted an experiment in urban South India to examine the barriers faced by customers in purchasing a low-cost savings product. We found that banks have a high ability to influence financial access outcomes, even when product availability and eligibility rules are non-discretionary. Nearly all banks refused to market the regulator-mandated basic accounts, despite the customers being atypically persistent in asking for “basic accounts”. Additionally, in more than half (55%) of the bank branches visited, customers were turned away when they attempted to negotiate for an alternative, affordable savings product: in half of the cases, the bank refused to accept the customer’s valid identity or address proof, while in the other half of the cases, the bank refused to market an alternative low-cost product. For the accounts that were opened, the banks demanded excessive identity and address documents, withheld key information about the product’s terms and fees, and imposed significant time, effort, and incidental costs on the customers. Given the benefits of low-cost accounts and their linkage to the Indian government’s broader financial inclusion goals, our findings suggest a need for careful monitoring and targeted enforcement of India’s financial inclusion policy implementation.”

Amy Jensen Mowl and Camille Boudot (2014). Barriers to Banking: Results from an Audit Study in South India. NSE Working Paper Series No. WP-2014-1.

Read the full paper here: http://www.nseindia.com/research/content/NSE-IFMR_Paper_5.pdf

30
Sep

How Much Can Asset Portfolios of Rural Households Benefit from Formal Financial Services?

As part of the NSE-IFMR ‘Financial Inclusion’ Research Initiative, Vishnu Prasad, Anand Sahasranaman, Santadarshan Sadhu, and Rachit Khaitan of IFMR Finance Foundation have authored a working paper for the series. Using customer data from a financial services institution that operates in remote rural districts of India, the authors constructed stylised typologies of household asset portfolios based on primary and secondary sources of income. Despite a demonstrated demand for various financial services, this study finds that the asset portfolio of the average rural household in India is composed almost entirely of two physical assets—housing and jewellery. A comparison with a hypothetical portfolio of financial assets reveals that rural households could earn significantly higher levels of real returns, the increase ranging from 2.02% to 4.97%, at the extant levels of risk.

The results point to the urgent policy imperative to provide rural households with access to financial instruments that would allow them to construct a more diversified, tradable, and liquid portfolio offering higher yields and shelter from local market fluctuations.

Click here to read the full paper.

15
Sep

Sustainable Financing for Indian Cities

By Anand Sahasranaman & Vishnu Prasad, IFMR Finance Foundation

A version of this article was first published in the September 2014 edition of the monthly journal Yojana.

Municipal finances in India are characterised by the constant tension between the funds and functions of local governments. Cities in India have insufficient revenue tools to meet their expenditure requirements. While the 74th Constitutional Amendment Act (CAA) devolved a great deal of functional autonomy to local governments, a commensurate devolution of financial autonomy was absent. Out of the 18 functions to be performed by municipal bodies under the 74th CAA, less than half have a corresponding financing source. Furthermore, most local governments cannot set tax rates or change the bases of collection without the explicit concurrence of state governments. However, not all problems of municipal financing in India are attributable to the upper tiers of governments. Local governments have failed to utilize adequately even those tax and fee powers that they have been vested with, in particular by failing to put forth an adequate collection effort. The very low levels of own revenue generation in Indian cities have, thus, precluded them from providing even the most basic public services to their citizens.

While the thrust of urban policy in India has been on the metropolitan centres, the current state of public infrastructure and service delivery in India’s small and medium cities is, if anything, even more alarming than that in the larger ones. The central question that therefore confronts us, in the context of cities both big and small, is this: How can cities sustainably finance the development of public infrastructure to ensure service delivery that conforms to the laid-out benchmarks for all citizens in the next fifteen years? This article argues that in order to meet this challenge, Indian cities will need to increasingly generate higher levels of own source revenue and efficiently use market based financing mechanisms to ensure minimum levels of service delivery.

To read the full article please click here.