| Publication type: | Book part |
| Type of review: | Editorial review |
| Title: | Large-scale data-driven financial risk assessment |
| Authors: | Breymann, Wolfgang Bundi, Nils Heitz, Jonas Micheler, Johannes Stockinger, Kurt |
| DOI: | 10.1007/978-3-030-11821-1_21 |
| Published in: | Applied data science : lessons learned for the data-driven business |
| Editors of the parent work: | Braschler, Martin Stadelmann, Thilo Stockinger, Kurt |
| Page(s): | 387 |
| Pages to: | 408 |
| Issue Date: | 14-Jul-2019 |
| Publisher / Ed. Institution: | Springer |
| Publisher / Ed. Institution: | Cham |
| ISBN: | 978-3-030-11820-4 978-3-030-11821-1 |
| Language: | English |
| Subjects: | Stress test; Big data; Simulation; Financial risk |
| Subject (DDC): | 005: Computer programming, programs and data 332: Financial economics |
| Abstract: | The state of data in finance makes near real-time and consistent assessment of financial risks almost impossible today. The aggregate measures produced by traditional methods are rigid, infrequent, and not available when needed. In this chapter, we make the point that this situation can be remedied by introducing a suitable standard for data and algorithms at the deep technological level combined with the use of Big Data technologies. Specifically, we present the ACTUS approach to standardizing the modeling of financial contracts in view of financial analysis, which provides a methodological concept together with a data standard and computational algorithms. We present a proof of concept of ACTUS-based financial analysis with real data provided by the European Central Bank. Our experimental results with respect to computational performance of this approach in an Apache Spark based Big Data environment show close to linear scalability. The chapter closes with implications for data science. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/4501 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) Institute of Data Analysis and Process Design (IDP) |
| Published as part of the ZHAW project: | Large Scale Data-Driven Financial Risk Modelling |
| Appears in collections: | Publikationen School of Engineering |
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Breymann, W., Bundi, N., Heitz, J., Micheler, J., & Stockinger, K. (2019). Large-scale data-driven financial risk assessment. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 387–408). Springer. https://doi.org/10.1007/978-3-030-11821-1_21
Breymann, W. et al. (2019) ‘Large-scale data-driven financial risk assessment’, in M. Braschler, T. Stadelmann, and K. Stockinger (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 387–408. Available at: https://doi.org/10.1007/978-3-030-11821-1_21.
W. Breymann, N. Bundi, J. Heitz, J. Micheler, and K. Stockinger, “Large-scale data-driven financial risk assessment,” in Applied data science : lessons learned for the data-driven business, M. Braschler, T. Stadelmann, and K. Stockinger, Eds. Cham: Springer, 2019, pp. 387–408. doi: 10.1007/978-3-030-11821-1_21.
BREYMANN, Wolfgang, Nils BUNDI, Jonas HEITZ, Johannes MICHELER und Kurt STOCKINGER, 2019. Large-scale data-driven financial risk assessment. In: Martin BRASCHLER, Thilo STADELMANN und Kurt STOCKINGER (Hrsg.), Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 387–408. ISBN 978-3-030-11820-4
Breymann, Wolfgang, Nils Bundi, Jonas Heitz, Johannes Micheler, and Kurt Stockinger. 2019. “Large-Scale Data-Driven Financial Risk Assessment.” In Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger, 387–408. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_21.
Breymann, Wolfgang, et al. “Large-Scale Data-Driven Financial Risk Assessment.” Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler et al., Springer, 2019, pp. 387–408, https://doi.org/10.1007/978-3-030-11821-1_21.
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