Are there asymmetries in the response of bank interest rates to monetary shocks?

Abstract

This article examines the velocity and asymmetry of the response of bank interest rates to monetary policy shocks. Using an asymmetric vector error correction model, it analyses the pass-through of changes in money market rates to retail bank interest rates in Italy in the period 1985–2002. The main results of the article are: (1) the speed of adjustment of bank interest rates to monetary policy changes increased significantly after the introduction of the 1993 Consolidated Law on Banking; (2) interest rate adjustment in response to positive and negative shocks is asymmetric in the short run, but not in the long run; (3) banks adjust their loan (deposit) rate faster during periods of monetary tightening (easing); (4) this asymmetry almost vanished since the 1990s.

Acknowledgements

We wish to thank one anonymous referees for helpful comments. We also thank Heinz P. Galler, Giorgio Gobbi, Guay Lim and participants at seminars held at the Bank of Italy and the Halle Institute for Economic Research for discussions and comments. The usual disclaimer applies. The opinions expressed in this article are those of the authors only and in no way involve the responsibility of the Bank of Italy.

Notes

¹ Among cross-country studies, see Cottarelli and Kourelis (1994), Borio and Fritz (1995) and de Bondt et al. (2005). Among national studies see Cottarelli et al. (1995) and Angeloni et al. (1995) for Italy, Weth (2002) for Germany, and Berlin and Mester (1999) for the US.

² Before 1987 the Bank of Italy authorized the opening of new branches on the basis of a 4-year plan reflecting estimated local needs for banking services.

³ The 1993 Consolidated Law on Banking, introduced in September, completed the enactment of the institutional, operational and maturity despecialization of the Italian banking system and ensured the consistency of supervisory controls and intermediaries’ range of operations with the single market framework. The business restrictions imposed by the 1936 Banking Law, which distinguished between banks that could raise short-term funds (‘aziende di credito’) and those that could not (‘Istituti di credito speciale’), was eliminated. For more details see the Annual Report of the Bank of Italy for 1993.

⁴ Data are available on the Internet site of the Bank of Italy (www.bancaditalia.it).

⁵ Deposit interest rate rigidity in the 1980s has been extensively analysed for the US as well. Among the market factors that have been found to affect the responsiveness of bank deposit rates are the direction of the change in market rates (Hannan and Berger, 1991; Ausubel, 1992), if the bank interest rate is above or below a target rate (Moore et al., 1990; Neumark and Sharpe, 1992; Hutchison, 1995) and market concentration in the bank deposit market (Hannan and Berger, 1991). Rosen (2001) develops a model of price settings in the presence of heterogeneous customers explaining why bank deposit interest rates respond sluggishly to some extended movements in money market rates but not to others. Hutchison (1995) presents a model of bank deposit rates that includes a demand function for customers and predicts a linear (but less than one-to-one) relationship between market interest rate changes and bank interest rate changes. Green (1998) claims that the rigidity is due to the fact that bank interest rate management is based on a two-tier pricing system; banks offer accounts at market-related interest rates and at posted rates that are changed at discrete intervals.

⁶ The monetary policy interest rate has been considered as an exogenous variable. This hypothesis has been tested in a VAR model where all interest rates are treated as endogenous variables. The null hypothesis of weak exogeneity of the monetary policy indicator has been accepted with a p-value of 20.5%. Following Harris (1995), we have therefore removed the equation for the monetary policy indicator from the system.

⁷ The first one du90, reflects Bank of Italy interventions soon after capital movement liberalization (May 1990). ‘In June, to prevent liquidity conditions from becoming excessively tight, the Bank of Italy made gross temporary purchases of securities in the secondary market totaling 21 trillion lire’. In September, the market was not attracted by medium-term securities. ‘With the aim of redirecting demand towards the longer end of the market, the Bank of Italy supplied only a very small quantity of these instruments for a short period lasting until the middle of the month. This caused liquidity to become abundant and banks’ excess reserves averaged around 8 trillion lire in the first two ten-day periods of September. The REPO rate fell to 6.7%’. ‘In October there was a net foreign exchange outflow of 2.3 trillion lire despite the placement of a 1 billion ecu bond issue abroad. The central bank counteracted a substantial creation of liquidity through the Treasury current account by making temporary security sales of 13.1 trillion lire at rates of around 11% which were appreciably higher than the rates prevailing in September’ (see Bank of Italy, ‘Economic Bullettin’, February 1991, pp. 37–39). Du90 was set to +1 in 90:6 and 90:9 and to −1 in 90:10. The second dummy, du92, reflects central bank operations during the 1992 currency crisis. After two increases in the official discount rate (from 12 to 13.75% in July and to 15% in September) in order to maintain the ERM parity monetary conditions were relaxed in November (from 15 to 13%) after Italy left the ERM. In order to capture monetary policy behaviour, dum92 has a −1 on 92:7 and 92:9, and a +1 on 92:11. It is worth noting that du90 and du92 gave a better result than using five point dummies (one for each date discussed above) coupled with a considerable gain in efficiency.

⁸ The convergence dummy dumco takes the value 1 between 1995:03 and 1998:09 and zero elsewhere. It represents a monetary policy stance geared to achieve the convergence of Italian interest rates towards those prevailing in the euro area.

⁹ This is the most natural and simple way to extend the VECM model, which is typically used to study the interest rate transmission mechanisms (Amisano et al., 1997) and the determinants of loans (Calza et al., 2003). Among other methods used to deal with asymmetries in different contexts, see the two-step procedure employed by Cover (1992) and the discrete-state Markov switching approach in Hamilton (1989, 1990).

¹⁰ The likelihood ratio for α _{D 2} = α _{D 2}* = α _{L 1} = α _{L 1} * = 0 is given by χ² (4) = 5.57 with a p-value of 0.23.

¹¹ As for the test β _L  = β _B  = 1, the likelihood ratio test statistic is 0.981 with a p-value of 0.612 in the first period and 0.402 with a p-value of 0.818 in the second period.