IT systems of banks and investment funds are versatile. They have been grown for years and today financial institutions increasingly rely on the quality of their quantitative technology. Such a trend has made computational finance topmost important.
Computational finance is a cross-disciplinary field that focuses on the financial services industry and relies on mathematical finance, numerical methods and computer simulations to make investment decisions and facilitates portfolio risk management.
Despite the fact banks tend to develop risk valuation systems in-house all of them can generally fit into the given below schema.
As the figure implies, there could be several inbound data streams but the most obvious ones are for trade and market data.
The Data 'Time Machine' layer isolates the components of the platform from the data sources and provides a single and unified point of access.
The scale of tasks solved by the risk platform is huge starting from risk measures estimates to cash flow generation and PnL forecasting. All these scenarios can be mathematically represented as a 'graph' with the following nodes:
Some of the tasks are computationally intensive. For this reason the system executes them in parallel usually on a GRID infrastructure.
Computational finance is a cross-disciplinary field that focuses on the financial services industry and relies on mathematical finance, numerical methods and computer simulations to make investment decisions and facilitates portfolio risk management.
Despite the fact banks tend to develop risk valuation systems in-house all of them can generally fit into the given below schema.
How the system works
The figure above shows how a generic risk valuation platform might support historical and real-time valuation scenarios.As the figure implies, there could be several inbound data streams but the most obvious ones are for trade and market data.
The Data 'Time Machine' layer isolates the components of the platform from the data sources and provides a single and unified point of access.
The scale of tasks solved by the risk platform is huge starting from risk measures estimates to cash flow generation and PnL forecasting. All these scenarios can be mathematically represented as a 'graph' with the following nodes:
- data injection
- model building
- simulation scenario generation
- trade & risk valuation
- result aggregation & transformation
Some of the tasks are computationally intensive. For this reason the system executes them in parallel usually on a GRID infrastructure.
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