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Maturity Structure of Banking Transactions and Its Role in Predicting Negative Net Worth of Banks

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  • Mikhail Mamonov

    (Institute of International Studies, MGIMO-University; CERGE-EI)

Abstract

In this paper, we perform a microeconomic analysis of positive and negative imbalances in the maturity structure of Russian banks’ transactions. In particular, using Heckman selection models at the cross-section of Russian banks, we test the ability of such imbalances to predict the probability of the detection of banks’ negative net worth and its expected magnitude in advance (three months before negative worth detection). The estimation results show that, first, certain indicators of imbalances do offer ‘value added’ in predicting ‘holes’ in banks’ capital: taking into account these imbalances in banks’ short- and medium-term transactions with households and short-term transactions with enterprises improves the quality of out-ofsample forecasts. Second, the very division into positive and negative imbalances makes sense: the effects are in many cases found to be opposite with respect to the size and likelihood of negative net worth detection at banks. Third, a separate analysis of banking transactions with households and those with businesses is also of great importance: the effect of imbalances in transactions similar in maturity structure but with different types of economic agents is in many cases opposite in sign. The results may be useful for the Bank of Russia in identifying potentially fragile banks as part of its prudential policy.

Suggested Citation

  • Mikhail Mamonov, 2020. "Maturity Structure of Banking Transactions and Its Role in Predicting Negative Net Worth of Banks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(2), pages 70-100, June.
  • Handle: RePEc:bkr:journl:v:79:y:2020:i:2:p:70-100
    DOI: 10.31477/rjmf.202002.70
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    References listed on IDEAS

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    1. Beck Thorsten & Büyükkarabacak Berrak & Rioja Felix K. & Valev Neven T., 2012. "Who Gets the Credit? And Does It Matter? Household vs. Firm Lending Across Countries," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-46, March.
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    7. Mamonov, M., 2018. "Lending Channel of Monetary Policy in Russia: Microeconomic Estimates for Retail and Corporative Segments of Credit Market," Journal of the New Economic Association, New Economic Association, vol. 37(1), pages 112-144.
    8. Klaus Schaeck, 2008. "Bank Liability Structure, FDIC Loss, and Time to Failure: A Quantile Regression Approach," Journal of Financial Services Research, Springer;Western Finance Association, vol. 33(3), pages 163-179, June.
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    Cited by:

    1. Bekirova, Olga & Zubarev, Andrey, 2023. "Determinants of risk, profitability and default probability of Russian banks," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 71, pages 20-38.

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    More about this item

    Keywords

    banks; balance falsifications; negative net worth; Heckman selection model; out-of-sample forecast;
    All these keywords.

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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