Fitting random cash management models to data

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1. Verfasser: Salas-Molina, Francisco
Format: Preprint
Veröffentlicht: 2024
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author Salas-Molina, Francisco
author_facet Salas-Molina, Francisco
contents Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models.
format Preprint
id arxiv_https___arxiv_org_abs_2401_08548
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Fitting random cash management models to data
Salas-Molina, Francisco
Computational Finance
68T01, 91B28
Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models.
title Fitting random cash management models to data
topic Computational Finance
68T01, 91B28
url https://arxiv.org/abs/2401.08548