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Authors: Petra Ristau and Lukas Krain

Affiliation: JRC Capital Management & Research GmbH, Kurfürstendamm 186, 10707 Berlin and Germany

Keyword(s): Financial Risk Measures, Value-at-Risk, Expected Shortfall, Monte-Carlo Simulations.

Abstract: This paper will examine the possibility of real-time risk calculations within the financial services industry. Due to regulatory standards, this paper will focus mainly on the calculations of value-at-risk (VaR) and expected shortfall (ES). Their computation currently requires simplified theory in order to be done within real-time. This demonstrates a real-world disadvantage to investment professionals since they need to comply with regulatory requirements when doing real-time decisions without knowing the accurate risk numbers at any one time. Within the CloudDBAppliance project, we designed an architecture that shall make real-time risk monitoring possible using cloud computing and a fast analytical processing platform.

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Paper citation in several formats:
Ristau, P. and Krain, L. (2019). Real Time Financial Risk Monitoring as a Data-intensive Application. In Proceedings of the 9th International Conference on Cloud Computing and Services Science - ADITCA; ISBN 978-989-758-365-0; ISSN 2184-5042, SciTePress, pages 660-665. DOI: 10.5220/0007905706600665

@conference{aditca19,
author={Petra Ristau. and Lukas Krain.},
title={Real Time Financial Risk Monitoring as a Data-intensive Application},
booktitle={Proceedings of the 9th International Conference on Cloud Computing and Services Science - ADITCA},
year={2019},
pages={660-665},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007905706600665},
isbn={978-989-758-365-0},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Cloud Computing and Services Science - ADITCA
TI - Real Time Financial Risk Monitoring as a Data-intensive Application
SN - 978-989-758-365-0
IS - 2184-5042
AU - Ristau, P.
AU - Krain, L.
PY - 2019
SP - 660
EP - 665
DO - 10.5220/0007905706600665
PB - SciTePress