The Duration of Bank Retail Interest Rates

Abstract

We examine the rigidity of retail deposit and loan rates by applying duration analysis on uniquely rich data. We find that the retail rate dynamics are state-dependent. An important determinant of the duration of retail interest rates are the dynamics of the wholesale (market and monetary policy) interest rates. We also show that the reaction to positive and negative wholesale interest rate changes is strongly asymmetric. Moreover, retail rate durations are significantly modified by bank and market characteristics, such as the size of the bank, its market share in a given local market, and its geographical scope.

Key Words:

• Interest Rate Rigidity
• Interest Rate Pass-Through
• Duration Analysis
• Hazard Rate

JEL classifications:

• G21
• E42

We thank Antonio Antunes, Diana Bonfim, Tim Dunne, Eduardo Engel, Roy Gardner, James Thomson, Jürgen von Hagen, and participants of the University of Bonn Macro-Workshop, Banco de Portugal Research Seminar, and the 2010 European Economic Association meetings for useful comments on earlier versions, and Monica Crabtree-Reusser for editorial assistance. Dinger gratefully acknowledges financial support by the Deutsche Forschungsgemeinschaft (Research Grant DI 1426/2-1). This research reflects the views of the authors and not necessarily the views of the Deutsche Bundesbank, the Federal Reserve Bank of Cleveland, or the Board of Governors of the Federal Reserve System.

Notes

1. We are only aware of two other studies applying duration analysis to interest rate dynamics: Arbatskaya and Baye, (2004) apply the method in a study of the duration of online mortgage rates, while Dinger, (forthcoming) studies the effect of bank mergers on retail rate dynamics using duration techniques.

2. Hofmann and Mizen, (2004) and De Graeve, De Jonghe, and Vander Vennet, (2007) relax the linear cointegration assumption and estimate nonlinear error-correction models as robustness checks. These still assume continuous adjustment, which is inconsistent with menu cost models.

3. Local markets are defined, in the tradition of the banking literature, as metropolitan statistical areas (MSAs).

4. The deposit rates subset of the data has been used in Craig and Dinger, (2009).

5. The same has been found in the interest rate pass-through literature (see de Graeve, De Jonghe, and Vander Vennet, 2007).

6. Note that these products are not of marginal importance for banks and consumers: deposits, checking accounts, and money market deposit accounts are the major source of retail funding for US banks; loans, personal loans, and credit cards are the asset categories most closely related to private consumption of nonhousing items.

7. The illustrated smoothed hazards use Stata’s default bandwidth for the kernel density smoothing, which is equal to the width that would minimize the mean integrated squared error if the data were Gaussian and a Gaussian kernel were used. As discussed in the Stata help reference, the default width could be too wide and oversmooth the density. We address this concern by re-estimating the hazard functions using a number of alternative bandwidths. The use of smaller bandwidths than the default one generates some local maxima after the 60th week, which are not visible in the case of the default bandwidths. Nevertheless this the key result of initially increasing and then decreasing hazards is robust to the choice of the bandwidth.

8. Changes in the wholesale interest rate can also be interpreted as marginal cost changes. Simple theoretical models of banking predict a positive dependence between bank retail deposit and loan rates and wholesale money market rates (see Kiser, 2004). These models assume that loans are the output in a production function that uses retail and wholesale funds as inputs. In other words, the effect of wholesale rate changes on loan rates resembles the effect of changing input prices on the prices of final goods. The effect of wholesale rate changes on deposit rates is motivated by the substitutability of retail deposits and wholesale funds. An alternative view of the production function of the bank assumes that banks issue deposits and sell the accumulated funds in the wholesale market. In that case, the wholesale rate is the price of output, whereas the retail rate is the input price. In both frameworks, an exogenous rise in the wholesale rate is related to an increase in the optimal retail deposit and loan rates offered by the bank. This interpretation, however, ignores a whole range of the bank’s noninterest rate costs.

9. The GARCH process is estimated for the differences in logarithms of the rates; in each case, all parameters are highly significant and are measured tightly. GARCH-estimated parameters are available from the authors on request.

10. As a robustness check, we also control for potential nonlinearities in the hazard rates’ reaction to market concentration; we split the sample into interest rates in highly concentrated bank markets and those in less-concentrated markets. Results are qualitatively the same.

11. In this paper, we focus on both the cross-sectional and time series variation of the bank and market characteristics included in the vector of covariates, without exploring the sources of this variation. Dinger, (forthcoming) explicitly focuses on the rigidity effects of the bank and market structure changes generated by bank mergers. For this purpose, she examines how the probability of a bank to change its deposit rates in the months following a bank merger depends on the characteristics of the merger.

12. The shared-frailty estimation approach aims at avoiding potential biases that can arise if a bank-specific pricing effect impacts pricing behavior in all local markets (see Nakamura and Steinsson, 2008, which applies a similar approach to control for heterogeneity across product groups). Results of the estimations do not significantly change if we do not account for the bank-specific effect and if we include a bank–market random effect rather that a bank random effect.

13. The effect of the relative hazard change is computed as 1.29 = exp[ln(0.283)0.5] exp[ln(5.8382)0.5].

14. The effect of the relative hazard change is computed as 0.53 = exp[ln(0.2823)0.5].

15. For the sake of brevity, the results of this estimation are not reported here.