Over the last two decades, financial derivatives markets experienced tremendous growth in market share, volume, sophistication and customization, all contributing to increased complexity of pricing and risk analysis of these securities. Ubiquitous economic growth lulled everyone into a false sense of security, while development of sophisticated risk analytics—and technologies enabling it—lagged. The status quo did not stimulate development of distributed computing to meet today’s sharp increase in demand for computational capacity in the financial sector. At present, as regulators debate what kinds of derivatives should be subjected to a battery of risk metrics and central clearing, firms are seeking ways to gain competitive advantage through adoption of computationally intensive analytics, to model an array of domestic and global events.
Analytics of securitized and derivative products is a challenge in that it is data-intensive, computationally expensive and must be done by the deadline. Concurrently, the industry demands faster computations, while IT budgets remain the same or have been reduced, presenting the proverbial business conundrum: how to do more with less. Albeit, the topology of these computations falls into the "embarrassingly...
- Acquisition of Etrali Fuels IPC’s Global Goals
- ESMA Seeks Data Experts for Reporting Working Group
- European Central Bank Taps SIA-Colt for Target2-Securities Connection
- Almax Enlists Victory Networks, Sumo Capital for AI News Platform Testing
- Markit Buys JPMorgan Integration Software for Syndicated Loans