Sovereign Wealth Funds (SWFs) play an important role in helping countries diversify their income streams by investing in growth areas, such as equities, bonds and real estate. This is especially important for countries that may rely heavily on one commodity, such as oil.

This SWF has been making substantial investments in its talent base by growing its team of quantitative analysts and strategists responsible for creating and validating models, conducting research and developing new trading strategies. Members of the expanding team have been adding new datasets to support their analysis and were interested in evaluating market derived signals based on credit default swaps (CDSs) to capture the market's view about a company's perceived risk.

A CDS is a contract between a seller and a buyer that obligates the seller, in exchange for a premium (spread) paid by the buyer, to insure the buyer against a loan default or other credit event. CDS pricing is driven by market forces, which are constantly assessing and quantifying any changes in a borrower’s likelihood to repay its debt. As such, they can serve as early warning signs of potential stress, pinpointing problems before they appear in more traditional securities and their longer-term credit ratings. Crucially, the likelihood of default is a key determinant of fixed Income pricing, which should factor In, typically, a premium to offset the expected loss that an Investor would entail should a default occur.

Pain Points

Members of the quantitative investment team were building up their arsenal of information and analytical tools and wanted data that would help create an implied credit rating based on signals that reflected the market's daily view of credit risk. These market derived signals would enable them to:

• Capture the latest market sentiment about a company's perceived risk to support efficient credit risk-driven analysis.
• Understand differences between a fundamental view of risk based on credit ratings, which are considered as independent opinions of creditworthiness, and the market's view of risk in order to identify potential pricing arbitrage opportunities.
  
  

The data would need to be efficiently delivered to the team's internal system. In addition, it was important that the methodology be based on a well-known and trusted source. Given this, the team contacted S&P Global Market Intelligence ("Market Intelligence") to learn more about the firm's offering.

The Solution

Specialists from Market Intelligence described data generated by Market Derived Signals, a sophisticated model based on a methodology developed by S&P Global Ratings^[1] that enables effective proactive credit risk monitoring on a daily basis. The solution would enable the team to:

	Capture early warning signals of credit risk	Market Derived Signals is a statistical model that provides a point-in-time view of credit risk for both rated and unrated entities by evaluating daily CDS spreads to provide an early warning sign of potential credit changes. The model captures the market’s view about a company's perceived risk, which can vary from a longer-term fundamental view, underscoring both opportunities and risks.
	Efficiently deliver data generated by Market Derived Signals to an internal system	XpressfeedTM automates the download and management of data, enabling delivery as needed in a ready-to-query relational database to link to internal applications.

Key Benefits

Members of the quantitative team felt that data from Market Derived Signals would be an important addition to their analytical toolkit and subscribed to the offering to complement their access to credit ratings and internal models. They are now benefiting from having:

• A robust overlay to CDSs developed by a renowned rating agency to assess the market's daily perception of credit risk.
• Credit risk analysis on a real-time basis as the market moves, along with early warning signals of potential credit deterioration.
• Ready indicators that show where the market is not aligned with longer-term credit ratings to identify pricing arbitrage or divestment opportunities.
• Flexible data delivery via Xpressfeed to easily comingle internal and external data.
• Detailed user guides and technical documentation for the model, plus 24x7 support.
  
  

Click here to explore some of the datasets mentioned in this Case Study.

^{[1] S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence probability of default (PD) credit model scores from the credit ratings issued by S&P Global Ratings.}