Optimizing IBM algorithmics' mark-to-future aggregation engine for real-time counterparty credit risk scoring

The concept of default and its associated painful repercussions have been a particular area of focus for financial institutions, especially after the 2007/2008 global financial crisis. Counterparty credit risk (CCR), i.e. risk associated with a counterparty default prior to the expiration of a contract, has gained tremendous amount of attention which resulted in new CCR measures and regulations being introduced. In particular users would like to measure the potential impact of each real time trade or potential real time trade against exposure limits for the counterparty using Monte Carlo simulations of the trade value, and also calculate the Credit Value Adjustment (i.e, how much it will cost to cover the risk of default with this particular counterparty if/when the trade is made). These rapid limit checks and CVA calculations demand more compute power from the hardware. Furthermore, with the emergence of electronic trading, the extreme low latency and high throughput real time compute requirement push both the software and hardware capabilities to the limit. Our work focuses on optimizing the computation of risk measures and trade processing in the existing Mark-to-future Aggregation (MAG) engine in the IBM Algorithmics product offering. We propose a new software approach to speed up the end-to-end trade processing based on a pre-compiled approach. The net result is an impressive speed up of 3--5x over the existing MAG engine using a real client workload, for processing trades which perform limit check and CVA reporting on exposures while taking full collateral modelling into account.