It begins with quality information. Risk measurement requires high-quality historical information for modeling. Many structured products and other derivatives have special features, including intricate priorities of payment, multiple hedges, complex definitions and multiple cash flow triggers that impact risk. Unfortunately, this data is often locked up in PDF documents and is usually incomplete. This means that someone is gathering this information manually and entering it by hand.
Some firms seek to address this issue by sourcing this manual process through a third party. While there are some processes that can be highly automated, many processes are still currently manual. It is critical that a firm contemplating outsourcing these processes ensure that the service provider has the proven experience, platform, processes, people and tight service-level agreements (SLAs) in place to back up any information the outsourcing partner may provide.
After ensuring the quality of data, a firm must be able to accurately model this information. Again, this becomes tricky in dealing with structured products such as CDOs that themselves may contain other pooled instruments. The underlying data model must be able to aggregate information from the firm level with look-through analysis down to its lowest elements.
When banks view data for such complex products, they tend to aggregate information across their organizations to understand the relationship between systemic, liquidity and counterparty risk. This information is then viewed with performance attribution to determine how well their managers adjusted to market changes.
Therefore, to be successful, the data model used for this aggregation must be able to incorporate all relevant enterprisewide information. It is critical for a firm to make certain that whoever is going to design this data model understands how enterprisewide information relates to one another -- especially if they wish to leverage sophisticated business intelligence technology for analysis and reporting.
A chief risk officer must have a solution that analyzes information across the firm all the way down to the underlying elements of a strategy or position. This capability must be flexible to allow the user to follow a chain of thought in their analysis. And it must allow analysis across multiple dimensions, such as time, risk, asset allocation and performance attribution.
In addition to internal use, many financial services firms are being asked to provide this data for reporting to regulatory bodies for macro-prudential risk analysis or to their clients, which are demanding greater transparency to their underlying funds. These trends will only gain significant momentum.
Netik is a global provider of financial data management services and products to the securities industry.