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BSDS—Balance Sheet Dynamics Simulator (Application ABM)

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Abstract

Disrupting events like COVID-19, climate change or new competitors (e.g., GAFAM) can permanently change the structure of a bank’s balance sheet and the bank’s risk profile. Agent-based modeling (ABM) is a versatile, interdisciplinary bottom-up approach that can be used to consider such effects in dynamic simulations of the balance sheet development. The authors present a concept for an agent-based model that simulates the effects of macroeconomic scenarios and competitive boundaries on the balance sheet dynamics of banks. An implementation of such a model could be used to explore stylized balance sheet developments over time and thereby provide a valuable planning tool for qualitative and quantitative risk management.

Keywords

  • Balance sheet
  • Agent-based modeling
  • ABM
  • Balance sheet dynamics
  • Balance sheet development
  • Credit risk

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Notes

  1. 1.

    VUCA—volatility‚ uncertainty, complexity, ambiguity.

  2. 2.

    European Banking Authority stress test (see European Central Bank 2020), for the methodology in 2021 see European Banking Authority (2020).

  3. 3.

    DFAST—Dodd-Frank Act Stress Test, part of the Dodd-Frank Act (see US Congress 2010).

  4. 4.

    Comprehensive Capital Analysis and Review (see BOARD OF GOVERNORS of the FEDERAL RESERVE SYSTEM 2015) and the unpublished Comprehensive Liquidity Analysis and Review.

  5. 5.

    GAFAM & BATX.

  6. 6.

    At the end of the Little Fable the mouse gets trapped and eaten by the cat.

  7. 7.

    Some banks still rely on a yearly planning process with forecast adjustments on a quarterly basis. They do not consider many well-known dependencies of macroeconomic dynamics on demand and supply.

  8. 8.

    Vector Auto Regression Model (see Lütkepohl 2005).

  9. 9.

    Multivariate GARCH Model (see Bauwens et al. 2006).

  10. 10.

    Recurrent Neural Network (see Section 3.2 in Liermann et al., Deep Learning—an Introduction 2019).

  11. 11.

    See Altman, Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy (1968) for the original paper and (Altman et al. 2014) for a more recent analysis.

  12. 12.

    See Section 3.7.1 in Liermann et al., Mathematical Background of Machine Learning (2019).

  13. 13.

    Feedforward neural network (see Section 2.1.2 in Liermann et al., Deep Learning—an Introduction 2019).

  14. 14.

    Risk weighted assets.

  15. 15.

    In an early stage of the Financial Navigator, the balance sheet was assumed to be constant like in the EBA stress test, by replacing the matured loans.

  16. 16.

    Gross domestic product, the most common indicator for the macroeconomic development.

  17. 17.

    More information on application of networks can be found in Enzinger and Grossmann (2019).

  18. 18.

    The Financial Navigator can use a Vector Auto Regression Model (VAR), an M-Garch model and an LSTR Neural Network to generate scenario paths for the major macroeconomic indicators and is flexible to incorporate other models.

  19. 19.

    Gross domestic product.

  20. 20.

    The maximum growth of a bank’s balance sheet per year could be derived for the macroeconomic development.

  21. 21.

    Commercial real estate.

  22. 22.

    Residential real estate.

  23. 23.

    The retail sector is only modeled by groups of clients because a modeling of each retail client would lead to a system size with huge computational effort.

  24. 24.

    A more sophisticated setting would even allow the clients to act in different markets simultaneously (e.g., a retail client can demand installment loans and a mortgage).

  25. 25.

    Gross domestic product.

  26. 26.

    In this simple model, we wish to account for the mere existence of a price dynamic, but we are not interested in the equilibrium price and its realistic contributions that are mentioned in the following paragraph.

  27. 27.

    Probability of default.

  28. 28.

    To understand the potential of ABM, it is an easy thought experiment to alter the first five components by banking group and see how this will affect the market structure in the future periods (e.g., what happens when the funding costs for smaller banks rise? Will the smaller banks lose market share?).

  29. 29.

    The price bucket where the biggest credit volume is transacted.

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Liermann, V., Dittmar, H. (2021). BSDS—Balance Sheet Dynamics Simulator (Application ABM). In: Liermann, V., Stegmann, C. (eds) The Digital Journey of Banking and Insurance, Volume I. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78814-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-78814-8_9

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