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Nested MC-Based Risk Measurement of Complex Portfolios: Acceleration and Energy Efficiency

Author

Listed:
  • Sascha Desmettre

    (Department of Mathematics, University of Kaiserslautern, 67663 Kaiserslautern, Germany)

  • Ralf Korn

    (Department of Mathematics, University of Kaiserslautern, 67663 Kaiserslautern, Germany
    Department of Financial Mathematics, Fraunhofer ITWM, 67663 Kaiserslautern, Germany)

  • Javier Alejandro Varela

    (Microelectronic Systems Design Research Group, University of Kaiserslautern, 67663 Kaiserslautern, Germany)

  • Norbert Wehn

    (Microelectronic Systems Design Research Group, University of Kaiserslautern, 67663 Kaiserslautern, Germany)

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.

Suggested Citation

  • Sascha Desmettre & Ralf Korn & Javier Alejandro Varela & Norbert Wehn, 2016. "Nested MC-Based Risk Measurement of Complex Portfolios: Acceleration and Energy Efficiency," Risks, MDPI, vol. 4(4), pages 1-35, October.
    • Handle: RePEc:gam:jrisks:v:4:y:2016:i:4:p:36-:d:80731
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    References listed on IDEAS

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    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    2. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    3. Michael B. Gordy & Sandeep Juneja, 2010. "Nested Simulation in Portfolio Risk Measurement," Management Science, INFORMS, vol. 56(10), pages 1833-1848, October.
    4. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
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