The 20 Rising Stars of Fixed Income 2008

SPONSORED ARTICLE access a historical rating for that piece of collateral. At present, many firms don’t have such processes in place. Creating an Internal Database So, what must firms do? Until governments and regulatory bodies force agencies to be more transparent about their ratings, there is little that can be done about accessing ratings information. In the meantime, there is much that financial institutions can improve in terms of their own internal ratings databases: • Consolidate - Many firms already have some form of ratings and Committee on Uniform Security Identification Procedures (CUSIP) database, but the information is all too often kept in spreadsheets or Microsoft Access databases that are useless for risk management and modelling purposes. As a result of the credit crisis, quantitative researchers can no longer get by using a bare minimum of data. Therefore, firms are being forced to consolidate their master securities and CUSIP databases. • Maintain - Keeping up with ratings changes can be a key challenge for back offices. Often, firms won’t have access to any ratings, so they assign default ratings to a class of securities. This can cause problems for researchers when creating models – and firms need to be able to trace ratings and identify cases where they have been assigned by default. Financial institutions also need to work on the time it takes to get ratings information into their database and make it accessible to quantitative researchers. In some firms, this can take up to three months, even though they need it the next day to protect their business. • Cleanse - With a regularly maintained database in place, firms need to ensure the quality of the information stored. Imposing filters is a vital stage before information can be used for modelling. The Advantages of Using Vhayu As ratings become more and more important in the business of fixed income, it makes sense for firms to feed their internal database from a single engine that captures and stores their market data. This is where Vhayu Technologies can help. Our system provides an excellent repository for data capture and storage and helps sell-side institutions record securities information live or as an end-of-day batch. Filtering Models are only as good as the data you put into them, and feeding data into Velocity is easy. However, filtering over 10 years of ratings data can be a nightmare. That is why Velocity has a component for quantitative developers, based on Eclipse, that includes extract transfer load (ETL) tools to help customers identify and eliminate obsolete ratings and ensure the quality of the information held in their database. Rules and Filters It is not enough to simply store ratings data; it needs to be accessible. Quantitative researchers needed to be able to quickly find – and use – the pieces of data they want. Vhayu Technologies has data cleansing processes, including XML files and out of the box filtering mechaMAY 2008 nisms, in place. We also are continuously developing new features that will help customers deal with these issues. Collateral Allocation Engine Velocity also can be used to create a global collateral allocation engine that will facilitate greater firm-wide visibility into counterparty concentration and exposure for the purposes of accessing risk, streamlining the back office processing of repo transactions and enabling the firm’s treasury to optimize its use of collateral. Customers can establish a set of rules that match the most profitable client with the most profitable collateral. The same feature also can be used to help firms better analyse their cash position versus their portfolio. Portfolio Analysis Velocity’s data analysis capabilities make it easier for financial institutions to re-optimise their portfolio. By incorporating their ratings database into Velocity, firms can create a sound basis for the creation of an optimal portfolio and a rapid transition towards it. This approach could bring significant benefits to treasury departments of sell-side firms, hedge fund risk managers and/or researchers calculating VaR. Automation Using Velocity, firms can eliminate the need for manual ratings monitoring processes. Instead of using a team of people to identify bad ratings, they can create rules that will highlight suspect cases. This would leave the experts free to fix problems, instead of just identifying them. Looking Ahead The next stage is for financial institutions to gain full, real-time access to agency ratings. Today’s financial institutions want economic indicators in real time. They want to equip their researchers to model ratings changes - and what happens as a result - using historical ratings to predict future changes. The only way to do this is to gain live access to ratings and feed them into a real-time system, such as Velocity, which will help them perform real-time modelling. With this in place, financial institutions will be able to create an extensive, live database that will help them back test and develop more accurate models as ratings change. And, as a powerful data engine designed to handle millions of pieces of data, Velocity is well-placed to help firms store and filter this information to create a powerful modelling tool. Vhayu Technologies delivers the fastest and easiest-to-use real-time software solutions to the world's leading financial institutions for the capture and high-speed analysis of massive amounts of streaming and historical data. Through its unique patented technology, the Vhayu Velocity tick processing and persistence platform gives a significant competitive advantage to its customers by delivering the information needed to make trading decisions faster than any other available system. Partnered with Reuters, Microsoft, Intel and Infosys, Vhayu's customers include broker/dealers, hedge funds, market data providers and alternative trading systems. For more information, visit www.vhayu.com. FIXED INCOME RISING STARS 11 http://www.vhayu.com http://www.vhayu.com

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