Default risk charge (DRC) is a complex part of market risk frameworks. Learn how to navigate the DRC challenges posed by FRTB.
With less than two years remaining for banks to comply with the Fundamental Review of the Trade Book (FRTB), many are wrestling with how best to approach the default risk charge (DRC), one of the most complex and challenging components of the new market risk framework. In every case, data is crucial for the various simulations and calculations required to decide the most appropriate approach or combination of approaches.
Banks have the option to determine the minimum capital requirements for market risk using the standardised approach (SA), the internal model approach (IMA), provided the bank obtains approval on a desk level, or combinations of both. As we discuss below, the choice strongly depends on the structure of the portfolio. In some cases, the standardised approach is more than three times as capital intensive as the internal model approach.
To identify conditions under which the IMA tends to outperform SA (and vice versa) for DRC, which replaces the incremental risk charge (IRC) from Basel II.5, let’s explore both in more detail.
The Basel Committee on Banking Supervision (BCBS) issued the final version of the revised minimum capital requirements for market risk, also known as FRTB, in January 2016. While it is considered more advanced and precise, DRC presents more challenges by reducing degrees of freedom and variations compared to IRC in several ways:
In the standardised approach, BCBS explicitly describes the DRC model by prescribing fixed equations and factors that each bank has to use. In contrast, in the internal model approach BCBS only describes the mechanisms that need to be incorporated in the DRC model.
The capital impact of standardised approach versus internal model approach strongly depends on the structure of the portfolio (e.g., the size of the portfolio, the rating of the portfolio positions, etc.)..
For a large portfolio, which is usually well diversified, IMA-DRC tends to yield a lower capital charge than SA-DRC as the SA does not capture the correlation between the various assets. Let's review some specificities of the portfolio structure that are driving the differences between internal and standard approaches.
Distribution of rating: For portfolios with high concentration in positions rated ~BBB or worse, IMA-DRC tends to lead to higher capital charge than SA-DRC as a large portfolio position with IMA-PD >= 0.1% is more represented in the 99.9% VaR capital charge (since it defaults in the “worst” 0.1%). SA-DRC, in contrast, is based on an “average” loss calculation, which is less sensitive to tail events.On the other side of the rating spectrum, IMA-DRC is suited for portfolios with many top-rated positions despite the PD floor.
Correlation of assets: IMA embeds a correlation model that allows diversification benefits between all the assets. For a non-directional portfolio, i.e, with low or negative correlation, IMA-DRC is then better than SA-DRC as the standard approach does not capture the correlation, and only some hedging benefits within specific subsets (the buckets). Thus, a reduction in the average asset correlation does not reduce the capital charge in the standard approach. With IMA-DRC, the capital charge is decreasing, thanks to the incorporated correlation model.
Quality: For a granular portfolio, IMA-DRC tends to be less sensitive to rating fluctuations compared to SA-DRC. IMA-DRC is indeed less sensitive to a general rating deterioration than SA-DRC.
Diversification of seniority: For a diversified portfolio (equities and bonds), Both IMA-DRC and SA-DRC benefit from short equity positions offsetting long bond positions even if FRTB prescribes that a senior position cannot hedge a junior position.
Irrespective of the approach(es) taken, having the capacity to manage and aggregate a very large amount of data is a necessity, while the simulation capacity offers users the ability to explore multiple scenarios, especially when mixing both approaches. The complexity of calculations and allocations that arise when utilising both approaches simultaneously can be overwhelming.
Opensee’s platform, awarded FRTB Product of the Year last month, is uniquely positioned to support clients in addressing these challenges through its extensive range of features and functionalities. For more on why data is at the heart of risk management, click here.