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Modelling corporate bank accounts

Author

Listed:
  • Fry, John
  • Griguta, Vlad-Marius
  • Gerber, Luciano
  • Slater-Petty, Helen
  • Crockett, Keeley

Abstract

We discuss the modelling of corporate bank accounts using a proprietary dataset. We thus offer a principled treatment of a genuine industrial problem. The corporate bank accounts in our study constitute spare, irregularly-spaced time series that may take both positive and negative values. We thus builds on previous models where the underlying is real-valued. We describe an intra-monthly effect identified by practitioners whereby account uncertainty is typically lowest at the beginning and end of each month and highest in the middle. However, our theory also allows for the opposite effect to occur. In-sample applications demonstrate the statistical significance of the hypothesised monthly effect. Out-of-sample forecasting applications offer a 9% improvement compared to a standard SARIMA approach.

Suggested Citation

  • Fry, John & Griguta, Vlad-Marius & Gerber, Luciano & Slater-Petty, Helen & Crockett, Keeley, 2021. "Modelling corporate bank accounts," Economics Letters, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:ecolet:v:205:y:2021:i:c:s0165176521002019
    DOI: 10.1016/j.econlet.2021.109924
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    References listed on IDEAS

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    1. Anna Battauz & Marzia De Donno & Alessandro Sbuelz, 2012. "Real options with a double continuation region," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 465-475, April.
    2. John Fry & Matt Burke, 2020. "An options-pricing approach to election prediction," Quantitative Finance, Taylor & Francis Journals, vol. 20(10), pages 1583-1589, October.
    3. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169.
    4. Nassim Nicholas Taleb, 2018. "Election predictions as martingales: an arbitrage approach," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 1-5, January.
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    Cited by:

    1. Alan K. Karaev & Oksana S. Gorlova & Vadim V. Ponkratov & Marina L. Sedova & Nataliya S. Shmigol & Margarita L. Vasyunina, 2022. "A Comparative Analysis of the Choice of Mother Wavelet Functions Affecting the Accuracy of Forecasts of Daily Balances in the Treasury Single Account," Economies, MDPI, vol. 10(9), pages 1-27, September.

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    More about this item

    Keywords

    Corporate bank accounts; Fin Tech; Forecasting applications; Machine learning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • G1 - Financial Economics - - General Financial Markets
    • G3 - Financial Economics - - Corporate Finance and Governance

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