Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/167901
Authors: 
Desmettre, Sascha
Korn, Ralf
Varela, Javier Alejandro
Wehn, Norbert
Year of Publication: 
2016
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 4 [Year:] 2016 [Issue:] 4 [Pages:] 1-35
Abstract: 
Risk analysis and management currently have a strong presence in financial institutions, where high performance and energy efficiency are key requirements for acceleration systems, especially when it comes to intraday analysis. In this regard, we approach the estimation of the widely-employed portfolio risk metrics value-at-risk (VaR) and conditional value-at-risk (cVaR) by means of nested Monte Carlo (MC) simulations. We do so by combining theory and software/hardware implementation. This allows us for the first time to investigate their performance on heterogeneous compute systems and across different compute platforms, namely central processing unit (CPU), many integrated core (MIC) architecture XeonPhi, graphics processing unit (GPU), and field-programmable gate array (FPGA). To this end, the OpenCL framework is employed to generate portable code, and the size of the simulations is scaled in order to evaluate variations in performance. Furthermore, we assess different parallelization schemes, and the targeted platforms are evaluated and compared in terms of runtime and energy efficiency. Our implementation also allowed us to derive a new algorithmic optimization regarding the generation of the required random number sequences. Moreover, we provide specific guidelines on how to properly handle these sequences in portable code, and on how to efficiently implement nested MC-based VaR and cVaR simulations on heterogeneous compute systems.
Subjects: 
nested MC simulation
value-at-risk
conditional value-at-risk
heterogeneous compute systems
OpenCL
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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