NMDs Logistic Regression Model

Luca Cappellina () and Domenico Sartore ()
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Luca Cappellina: Ca' Foscari University of Venice; Gruppo BCC Iccrea Banca
Domenico Sartore: Ca' Foscari University of Venice

No 2025: 06, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: This paper proposes a novel approach to modeling the volumes of Non-Maturity Deposits (NMDs), a key component of interest rate and liquidity risk in the banking book. Using a multivariate logistic regression model with autocorrelated data, we estimate the probability of cash-out (PoC) from the stable portion of NMDs. Uniquely, our model relies solely on variables included in the six supervisory interest rate shock scenarios prescribed by the Basel Committee on Banking Supervision, ensuring consistency with regulatory stress testing frameworks and avoiding misalignment with macroeconomic scenario assumptions. Empirical results confirm that financial market conditions—particularly short-term interest rates and the yield curve slope—are significant drivers of withdrawal behavior, underlining the role of the interest rate channel in monetary policy transmission. Seasonal patterns and the impact of the COVID-19 crisis are also found to influence depositors' behavior. By isolating the contribution of supervisory variables, our approach supports both risk management practices and regulatory compliance, offering a practical and forwardlooking tool for banks in assessing core deposit stability under stressed conditions.

Keywords: Non-Maturity Deposits (NMDs); Interest Rate Risk; Liquidity Risk; Core Deposits; Logistic Regression; Supervisory Scenarios; Yield Curve Slope; Probability of Cash-out (PoC); Basel Framework; Stress Testing (search for similar items in EconPapers)
JEL-codes: C25 C33 E43 G21 G28 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2025
New Economics Papers: this item is included in nep-rmg
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