Stochastic analysis processes are random, where the input variables are determined at run time using a probability distribution and random number seed. They are typically repeated many thousands or even millions of times. Time series analysis and Monte Carlo simulations are good examples of this and used widely in predicting complex events whose interactions may not fully understood.
Example Projects/Use Cases
- IBM Netezza - A financial institution must calculate value-at-risk for an equity options desk. The IBM Netezza platform was able to run a Monte Carlo simulation on 200,000 positions with 1,000 underlying stocks (2.5 billion simulations) in under three minutes. Leveraging an in-database analytics approach allowed the financial institution to analyze the data where it resides, rather than build a parallel data-processing platform to run the simulation. Faster query response time—and eliminating the time required to move data between two platforms—allowed the company to add variables to investment strategy simulations, and to run the risk analysis more frequently.
- Murex MX3 - Headquartered in Paris France, and with offices throughout Europe, the USA and Asia-Pacific, Murex is one of Europe’s largest software developers, and one of the world’s leading providers of software and services to the financial sector. Its flagship product, Murex MX-3, is used for risk analysis in financial market trading, and has over 36,000 users at 200 institutions in 65 countries worldwide. Murex’s new HPC capability now allows the management of complex financial products in high precision, and in near real time, compared to a previous capability of only computing analytics once or twice a day.
Update:
- 2012.09.24 - Updated into jSeries
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