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2019 | OriginalPaper | Chapter

9. Intraday Liquidity: Forecast Using Pattern Recognition

Authors : Volker Liermann, Sangmeng Li, Victoria Dobryashkina

Published in: The Impact of Digital Transformation and FinTech on the Finance Professional

Publisher: Springer International Publishing

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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.

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previous chapter Predictive Risk Management
next chapter Internal Credit Risk Models with Machine Learning
Footnotes
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
van der Maaten, Accelerating t-SNE using tree-based algorithms (2014) and van der Maaten and Hinton, Visualizing High-Dimensional Data Using t-SNE (2008).
 
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.
Metadata
Title
Intraday Liquidity: Forecast Using Pattern Recognition
Authors
Volker Liermann
Sangmeng Li
Victoria Dobryashkina
Copyright Year
2019
Book
The Impact of Digital Transformation and FinTech on the Finance Professional

Print ISBN: 978-3-030-23718-9

Electronic ISBN: 978-3-030-23719-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-23719-6_9