In the novel monetary environment of negative interest policy rates (NIPR) in the Euro area, it is questionable whether the existing findings on determinants of Net Interest Margins (NIM) still hold. This paper analyzes differences in the development of NIM across business models represented by a set of three indicators prior to and after the introduction of NIPR. The strategies are based on a binary categorization between high and low levels of the business indicators using a median, 67-33 and 80-20 percentile cut-off rule. I use a difference in differences (DiD) estimation approach, even though NIPR impact all banks’ NIM. Thus, the obtained estimates do not measure the impact of NIPR itself, but the DiD between strategies. I mostly find positive albeit insignificant effects on banks with low asset held for trading, high deposit and customer loan ratios. In contrast, the DiD coefficient for banks with high deposit-based financing using an 80-20 cut-off is -14 bp, which proves to be a highly significant and economically relevant. These findings support the notion that multiple channels are affecting banks’ NIM.
Are Banks’ Net Interest Margins equally affected from Negative Interest Policy Rates? Evidence from the Euro Area.
“You can read Adam Smith, you can read [John Maynard] Keynes, you can read anybody and you can’t find a word to my knowledge on prolonged zero interest rates – that is a phenomenon nobody dreamed would ever happen.”
( Warren Buffet, 2016)
Since the onset of the Financial Crisis in 2007, many central banks have implemented exceptional measures lowering their key policy rates to approximately zero. Some central banks have even adapted negative rates (NIPR). Denmark was the first country to introduce negative rates on certificates of deposits from July 2012 through early 2014. Later in 2014, the ECB decided to set negative rates on their deposit facility. Some countries, e.g. Denmark, Switzerland, Japan and Sweden, followed the Euro area’s step into uncharted territory of NIPR. NIPR represent not only a new era for central banking, Kerbl and Sigmund (2017) also argue that they have the potential to be a game changer for the whole banking industry. To understand their significance, it is important to look at their impact on bank profitability.
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Figure 1: Schematic illustration of a bank's income statement and balance sheet structure, taken from Turk (2016).
Traditionally, one of the bank’s core functions is to provide liquidity by lending out long-term while refinancing short-term. For a loan with a longer maturity, banks can typically charge higher loan rates than what they pay for their funding. The generated profit, the Net Interest Income (NII), often represents a main source of a bank’s income. In this vein, the Net Interest Margin (NIM), the average margin a bank earns per intermediated unit of interest earning assets, remains a key indicator for bank profitability (Angbazo, 1997) and will be central element of this study. The main goal of this paper is thus to shed light on how banks NIM are affected by NIPR.
In practice, banks offer a wide range of services or specialize themselves. This becomes apparent when analyzing banks’ business strategies. Ayadi et al. (2015) closely monitor the strategies of European banks. They apply a cluster-analysis to aggregate the information of several business indicator variables each measuring a bank’s strategy along one dimension. I will replicate selected business indicator variables and transform them into binary strategy variables for values above the median, the 67 and 80 percentiles =1 (high) and below median, the 33 and 20 percentiles = 0 (low).
While my dataset covers accounting and financial reporting information for a sample of banks in Europe from 2006 to 2017, I will direct my empirical analysis solely on the Euro area, taking advantage of the policy change in mid-2014 at which point the ECB lowered the deposit facility rates to - 0.1 %. The sample is split along the binary variables in the year prior to the introduction of NIPR (2013). Further, I compute the differences of the two groups before the policy change (2011-2013) and after (2014-2017) to obtain the difference in differences. To estimate the means, I use regression analysis, controlling for country- and time-fixed effects and applying analytical weights rather than equally weighting banks. This approach is hence different from the bulk of other research papers on determinants of NIM, as it does not require data-sets over many periods or advanced dynamical models. Yet, it comes with the limitation that the results are only relative. The size or direction of the treatment itself cannot be determined.
Overall, I find that strategies associated with more conservative forms of banking (low ratio of assets held for trading, high ratio of deposit-based financing and of customer loans) fare slightly better than their counterparts. Apart from one specification, the differences are not significantly different from zero. There is one major exception to this, however. Using the 80-20 cut-off for the deposits to liability ratio, I obtain a highly significant and economically relevant difference of -14 bp relative to the low strategy. This suggests that there are conflicting channels at work through which NIPR impact the banks’ NIM which function to a varying degree depending on the bank’s strategy.
The paper proceeds as follows: Section 2 discusses the existing literature on determinants of NIM as well as mechanisms through which NIPR can have a heterogenous effect on NIM. Section 3 describes the sample and provides first descriptive evidence on heterogenous developments of NIM in the banking industry. In section 4, the econometric approach and the choice of business strategy indicators is motivated. The results are presented in Section 5. Section 6 concludes.
2 Literature Review
2.1 Determinants of NIM
Naturally, this paper is related to the vast literature on banking profitability focusing on NIM. Such studies are frequently based on the theoretical model of Ho and Saunders (1981).1 It assumes that banks act as risk-averse dealers that maximize their NIM (s). According to their model, banks set one rate for which they are willing to accept deposits ( and one for which they are willing to lend money ( . To engage in risky activities, banks require a profit mark-up (a, b) on the nominal interest rate. Quantities supplied and demanded are exogenous and follow a stochastic process:
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It is further shown that the optimal mark up on the interest rate depends, on the one hand, on monopoly power ( ) and, on the other hand, on the degree of absolute risk aversion of the bank management (R), the size of transactions (Q) and the volatility of nominal interest rates ( :
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The model was empirically tested and further developed. Most prominently in single-country studies, Allen (1988) introduces heterogeneity of loans into the dealership model, Angbazo (1997) incorporates credit risk and Maudos and Guevara (2004) add operating costs. In a cross-country set-up, Saunders and Schumacher (2000) use regulation and Brock and Saurez (2000) identify liquidity risk and macro variables like inflation and GDP growth as explanatory variables for NIM. Further factors that were employed as regressors include ownership (Claeys and Vennet, 2008), specialization and non-interest income (Carbo Valverde and Rodríguez, 2007).
In the aftermath of the financial crisis, research on the impact of monetary policy on NIM gained importance. In the standard textbook literature, changes of policy rates are typically discussed at normal rates. But what happens at low or even negative rates?
In a low interest rates environment, Borio et al. (2017); Claessens et al. (2016), Cruz-García et al. (2017); Genay and Podjasek (2014); Busch and Memmel (2017) uniformly present evidence for a quadratic relationship between short-term interest rates and NIM of banks. A marginal decrease of short-term interest rates at a low level is associated with a much stronger decline in NIM than at high levels.
Literature that explicitly links NIM and NIPR is rare and predominantly descriptive. One exception is Kerbl and Sigmund (2017) pointing out that one should not use the findings of studies focusing on low interest rates to make any inferences about the effect of negative interest rates, as the mechanisms might work differently. Using a simulation approach, they anticipate a much stronger negative impact of NIPR on NIM of the Austrian banking sector than predicted otherwise. Madaschi and Nuevo (2017) observe that in the case of Sweden and Denmark overall profitability of banks slightly increased, while NIM are resilient and stable. Eisenschmidt and Smets (2018) show that in the EU margins shrink overall, but the decline was much more pronounced in periphery countries, than core countries.
Sticky deposit rates: The central mechanism to whom the non-linear relationship between interest rates and NIM was attributed to is the “downward stickiness in deposit rates” (Turk, 2016). While banks in general seem to lack in their response of adjusting deposit rates relative to their loan rates, it is particularly relevant under NIPR. Referring to Ho and Saunders (1981) model, the bank is setting deposit rates depending on the key policy rate and the mark-up ,where is exogenously given by the central bank. Apart from depositing, bank customers have the option to hold the zero-interest yielding currency. Holding the currency is however associated with costs (e.g. storing, insurance, …) and benefits (immediacy, liquidity, safety). Whenever the utility of holding the currency is higher than the utility associated with depositing it, customer will prefer to hoard money. This is likely to vary between different customer groups e.g. (corporate vs. retail). Banks however benefit on deposits as a source of funding, since especially retail deposits are considered a very stable source (see Drechsler et al., 2018) and favorably treated under new liquidity regulation (e.g. the Net Stable Funding Ratio). Therefore, banks are assumed to have a lower bound on deposit rates. In an empirical study Eisenschmidt and Smets (2018) show that a representative subsample of German banks has been avoiding negative interest rates on retail deposits but has started partially transmitting negative rates to corporate clients, while at the same time loan rates further decreased.
1 Another theoretical framework that is sometimes applied in this context is the Klein-Monti model (Klein, 1971).
- Quote paper
- Valentin Stockerl (Author), 2019, Determinants of Net Interest Margins. Are Banks equally affeced by Negative Interest Policy Rates?, Munich, GRIN Verlag, https://www.grin.com/document/462285