Abstract
Intraday liquidity risk management has become of increased interest to regulators and financial professionals. Intraday liquidity and the related business processes are characterized by personal connections. Nonetheless, the aggregated view of the participants outside the bank can be clustered and assigned to certain patterns. Risk management always seeks to take an independent opinion in such an environment. This article focuses on how to detect patterns in intraday flows by customer. Additionally, the article shows how these patterns are used to forecast possible customer behavior and how to aggregate the forecast by currency. An improved decision basis is offered by combining possible and correlated client patterns at currency level not only by using machine learning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Like, for example, the value at risk schemata.
- 2.
Cumulative flow by day and intraday throughput are defined in Sect. 2.2.
- 3.
Introduction to black swans, see (Taleb, 2008).
- 4.
ICAAP—internal capital adequacy assessment process.
- 5.
ILAAP—internal liquidity adequacy assessment process.
- 6.
REPO —repurchase agreement.
- 7.
NMD —non-maturing deposits.
- 8.
LCR—liquidity coverage ratio.
- 9.
LVPS —large-value payment system.
- 10.
Literature
Basel Committee on Banking Supervision. (2013). Monitoring tools for intraday liquidity management (BCBS 248). Basel: BIS.
European Banking Authority. (2018). Guidelines on the revised common procedures and methodologies for the supervisory review and evaluation process/SREP) and supervisory stress testing. London: EBA.
Liermann, V., Li, S., & Schaudinnus, N. (2019). Mathematical background of machine learning. In V. Liermann & C. Stegmann (Eds.), The impact of digital transformation and fintech on the finance professional. New York: Palgrave Macmillan.
Taleb, N. N. (2008). The Black Swan: The impact of the highly improbable. New York: Random House.
van der Maaten, L. (2014). Accelerating t-SNE using tree-based algorithms. Journal of Machine, 15, 3221–3245.
van der Maaten, L., & Hinton, G. (2008). Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 The Author(s)
About this chapter
Cite this chapter
Liermann, V., Li, S., Dobryashkina, V. (2019). Intraday Liquidity: Forecast Using Pattern Recognition. In: Liermann, V., Stegmann, C. (eds) The Impact of Digital Transformation and FinTech on the Finance Professional. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-23719-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-23719-6_9
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-23718-9
Online ISBN: 978-3-030-23719-6
eBook Packages: Economics and FinanceEconomics and Finance (R0)