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IFRS 9 Implementation Challenge For Low Default Portfolios: One Possible Approach To Compute PDs

As of January 1, 2018, IFRS 9 will replace the current IAS 39 across several jurisdictions, including many European countries.

By focusing on expected credit losses, IFRS 9 will represent a significant shift from IAS 39 (incurred losses) since the new impairment requirements determine that expected losses will have to be computed not only for non-performing assets, but also for performing assets, with a direct impact on Profit and Loss (P&L).

Financial institutions, particularly banks, will be required to recognize an expected loss allowance of either 12-month expected credit loss for assets classified under stage one (performing) or lifetime expected credit loss for assets classified under stage two (under-performing).

Given the different requirements under IFRS 9 compared to the Basel requirements, adjustments to existing Basel-related systems, including data and models, will be a must in order to comply with IFRS 9.

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A particular challenge will arise regarding the computation of Probability of Default (PD) for IFRS 9 purposes for Low Default Portfolios (LDPs).

In order to address this challenge, S&P Global Market Intelligence has developed a tool that converts the PDs based on long-term average default rates (typically used for Basel IRB approach) into forward-looking PDs, taking into account current and future economic conditions. The adjustment factor, called the Z-Factor [1], will adjust the average observed long-run PDs to reflect a certain set of systematic scenarios thereby predicting the expected PDs for IFRS 9 purposes. The illustration for the computation is provided in the figure below:

Observed-predicted

Figure 1: Idealized Solution S&P Global Credit Cycle Projection Overlay

The overlay considers the following three-step approach: 

  1. Estimate Long-Term (LT) PDs 

To begin the process, one would need an assessment of the counterparty’s credit quality (i.e. a credit score or a rating), and the associated long-run PD for the score/rating. 

S&P Global Market Intelligence scorecards are credit quality assessment tools that  are developed based on S&P Global Ratings criteria and produce a forward-looking assessment, which can be mapped to probabilities of default (PDs) derived from observed default rates of ratings from S&P Global Ratings (over 36 years of default data). 

By using our scorecards, it is possible to cover low-data asset classes for all geographies (e.g. corporates, banks, insurers, other FIs, project/asset finance, and sovereigns), and obtain an assessment of the counterparty’s credit quality and respective PD.

  1. Determine Z-Factors 

The IFRS 9 standard would expect one to use PDs which resemble observed default rates to calculate expected losses, therefore the average long-run PDs derived in step one will need to be adjusted to meet IFRS 9 requirements.

The adjustment will take into account changes in the systematic environment, while company-specific changes are reflected in the credit assessment, which by definition is forward-looking. 

S&P Global Market Intelligence’s tool will compute the expected Z-Factor considering economic indicators, market indicators, and industry indicators, and benchmarking these to multiple Z-Factors. The final Z-Factor is computed as a weighted average of the Z-Factors derived for each indicator. 

  1. Derive Point-In-Time PDs 

Once forward-looking Z-Factors have been established, the final step involves adjusting LT PDs, LT (t), by the predicted Z-Factor to estimate the forward-looking IFRS 9 compliant PD for year t.

IFRS-9-PD

In order to obtain the lifetime PD term structure, the LT marginal PD for each future year is adjusted with a Z-Factor which is specific for that year. These marginal PDs are then combined to form a PiT cumulative term structure.

[1] The “Z-Factor” here denotes the adjustment performed using S&P Global Market Intelligence’s tool, and should not be confused with the “Altman’s Z-Score” used for measuring credit risk. 

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Jun 08, 2017
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