What is the project about?

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For compliance purposes, financial institutions are required to report on several financial risk measures. Any non-trivial calculation of risk, including the calculation of CVA, value-at-risk, or classic Greek risk factors are associated with a particular portfolio of assets and financial derivatives is based on a specification and simulation of the underlying assets and credit information about the involved. For portfolios with multiple such underlying assets, the processes that drive the asset quote generation are correlated and how they are correlated can be estimated from historical data.​

Risk calculations are often associated with performance problems, due to portfolios containing non-trivial assets and due to certain risk calculations, that require certain assets, to be treated as a derivative that optionally can default. These performance problems are particularly caused by the inherent need for nested simulation. These problems are apparent both for smaller institutions that serve as asset managers on behalf of private investors and for larger institutions and have difficulties to fulfill compliance obligations.​

To help solve the above-mentioned performance problems, we propose to apply massive parallelisation for supporting both nested simulations for calculating risk for advanced portfolios and for calculating CVA for portfolios containing less advanced assets. As part of this project and based on previous work we aim at constructing a prototype library for generating correlated asset price processes. As a result, the prototype library should then compute the relevant future market values for the relevant assets and output the simulated asset values.​

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Host Institution(s):​

Copenhagen University​

Principal Investigator:​

Martin Elsman Professor, Department of Computer Science​

Partners:​

Fine Analytics and KH Investment Partner​

Grant:​

200.000 DKK​​

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Looking for more?​

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A key component of a solution has been published as an open source package for solving linear programming problems in parallel (parallel revised simplex method) using the programming language Futhark:​

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https://github.com/diku-dk/optimise

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