Limitations imposed by the traditional practice in financial institutions of running risk analysis on the desktop mean many rely on models which assume a "normal" Gaussian distribution of events which can seriously underestimate the real risk. In this paper, we propose an alternative service which uses the elastic capacities of Cloud Computing to escape the limitations of the desktop and produce accurate results more rapidly. The Business Intelligence as a Service (BIaaS) in the Cloud has a dual-service approach to compute risk and pricing for financial analysis. In the first type of BIaaS service uses three APIs to simulate the Heston Model to compute the risks and asset prices, and computes the volatility (unsystematic risks) and the implied volatility (systematic risks) which can be tracked down at any time. The second type of BIaaS service uses two APIs to provide business analytics for stock market analysis, and compute results in the visualised format, so that stake holders without prior knowledge can understand. A full case study with two sets of experiments is presented to support the validity and originality of BIaaS. Additional three examples are used to support accuracy of the predicted stock index movement as a result of the use of Heston Model and its associated APIs. We describe the architecture of deployment, together with examples and results which show how our approach improves risk and investment analysis and maintaining accuracy and efficiency whilst improving performance over desktops. Heston Model Simulations; Heston Model; Business Intelligence as a Service (BIaaS); Calibration; APIs for stock index; Visualisation in the Cloud; SaaS in the private cloud. 1.
He selected the Heston Model and its associated API to compute predicted stock index movement and offer a good extent of accuracy REF .
The Business Intelligence as a Service in the Cloud
Future Gener. Comput. Syst.
Future Gener. Comput. Syst.
Computer Science