Towards Efficient Risk Quantification - Using GPUs and Variance Reduction Technique

Shih Hau Tan 1, 2
1 OASIS - Active objects, semantics, Internet and security
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués
2 TOSCA - TO Simulate and CAlibrate stochastic models
CRISAM - Inria Sophia Antipolis - Méditerranée , IECL - Institut Élie Cartan de Lorraine : UMR7502
Abstract : Value-at-Risk (VaR) provides information about global risk in trading. The request for high speed calculation about VaR is rising because financial institutions need to measure the risk in real time. Researchers in HPC also recently turned their attention on this kind of demanding applications. In this master thesis, we introduce two complementary and different strategies to improve VaR calculation: one is directly coming from financial mathematics, the other pertains to take advantage of high performance recently available computing devices: GPUs. Our aim is to study the potential of these two approaches on well chosen examples in order to evaluate how much computing time we can spare. Eventually, we discuss alternate approaches worth to be studied in future works.
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Shih Hau Tan. Towards Efficient Risk Quantification - Using GPUs and Variance Reduction Technique. Distributed, Parallel, and Cluster Computing [cs.DC]. 2013. ⟨hal-00932233⟩

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