Financial organizations are creating opportunities in the form of increasingly complex products. To create sophisticated products in the form of complex derivatives, one requires extensive analysis. One also requires dealing with exposures, interest rates, currency rates, mathematical and statistical models to accurately perform price discovery, risk calculation and estimate market direction. Exotic derivatives like Bermudan swaptions require the use of pricing models like the Brace-Gatarek-Musiela model and simulation using Monte Carlo techniques which require complex and time consuming calculations, putting enormous strain on the IT resources of a financial organization.
To come out with innovative products also requires extensive research of historical market statistics using techniques such as data mining. Data mining, by its nature is a storage intensive process, as it requires storing historical data spanning across various dimensions (time, markets etc.) as well as scales of magnitude.
Regulations such as Basel and Sarbanes-Oxley Act reiterate the need for a financial organization to comprehensively manage its risks, thus creating the need for a computationally intensive computing infrastructure with large storage so that operational, financial and accounting discrepancies can be detected and brought to the notice of the organization as soon as possible.
Nearly all financial organizations need to perform Portfolio Risk Calculations and Counterparty Credit Exposure calculations using various mathematical techniques which are computationally intensive and challenge the current IT infrastructure.
In times of increasing competition, organizations are trying to come up with accurate predictions and estimations in the shortest of times, thus enhancing the need for high throughput applications and platforms to support such applications. In addition, dramatic increase in the volume of trades being carried out through techniques such as Algorithmic trading has led to a demand for low latency infrastructure which can collect market data, analyze it and trigger events based on which orders can be sent to the street, all this happening within fractions of microseconds.
Financial organizations have already started to come up with some solutions to cope with the problems. Some technologies that have been deployed are being further explored to expand the IT capabilities of these organizations. Few of these technologies are Stream Processing, Cluster Computing etc.
Grid computing is a special type of parallel computing which involves complete computers/resources connected to each other in a loosely coupled fashion over a network using conventional network interfaces. It enables resource sharing to produce a large pool of usable resources, such as computing power, storage, network bandwidth, or even software licenses for third party applications and hides the complexity of the underlying architecture from the business user.
This pool of resources can be matched to dynamic business needs. A computing grid i.e. a grid used to harness the collective computing power of a network of computers can provide computing power comparable to that of a supercomputer. Thus sharing of resources is one of the primary characteristics of grid computing. It creates virtual resources which can be shared among a very wide range of users.
Diverse networks & resources make up a grid
The above grid contains trading terminals (Grid users) which harnesses the computing power and storage facilities of Workstations through multiple networks having different topologies. The database server is located in Delhi and file server in New York, but for the users of the Grid it is a single network. Grid Manager performs the functions of resource management, scheduling, and data management and provides security measures for data shared across the grid.
Advantages over conventional technologies
Areas of Application
Grid infrastructure has mainly been applied to applications with high processing requirements and low data requirements. But it is gradually making foray into areas where high data volumes are required. This capability has been extended because of the extension of the definition of a grid to include resources apart from computing power.
The functional areas and processes that can benefit from the utilization of Grid Infrastructure are:
Challenges
However there are certain challenges which an organization will face while deploying this infrastructure:
Limitations
Like all technologies, Grid also suffers from some limitations:
Grid technology, being a relative newcomer in the financial space, hasn’t had many takers yet. But Financial Service Organizations are gradually waking up to the benefits of leveraging their existing IT infrastructures to get optimal performance, while staying competitively cost-effective. The business drivers in this space namely competition, innovation and information make this technology all the more convincing.
Although an organization has to be wary of the potential drawbacks of such a technology and the challenges in deploying it across its entire infrastructure, it cannot sit back and watch while others reap enormous benefits out of a substantial investment early-on in this space. And as they say, it’s only the beginning…
This article has been authored by Sunny Nagpal from SPJIMR.
Image: FreeDigitalPhotos.net
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