Non-interest income management of banks in a global low interest rate environment
|Název práce v češtině:||Řízení neúrokového příjmu bank v prostředí nízkých úrokových sazeb|
|Název v anglickém jazyce:||Non-interest income management of banks in a global low interest rate environment|
|Klíčová slova:||banka a bankovnictví, bankovní business modely, bankovní neúrokový příjem, bankovní příjem z poplatků, měření bankovní ziskovosti, system GMM|
|Klíčová slova anglicky:||bank and banking, banking business models, bank performance measurement, fee income, non-interest income, System GMM|
|Akademický rok vypsání:||2014/2015|
|Typ práce:||diplomová práce|
|Ústav:||Institut ekonomických studií (23-IES)|
|Vedoucí / školitel:||prof. PhDr. Petr Teplý, Ph.D.|
|Řešitel:||skrytý - zadáno vedoucím/školitelem|
|Datum a čas obhajoby:||23.06.2016 08:30|
|Místo konání obhajoby:||IES|
|Datum odevzdání elektronické podoby:||13.05.2016|
|Datum proběhlé obhajoby:||23.06.2016|
|Oponenti:||Mgr. Hana Džmuráňová, Ph.D.|
|Seznam odborné literatury|
|1. DeYoung R., Rice T. (2004), Noninterest Income and Financial Performance at U.S. Commercial Banks, The Financial Review, Vol. 39, pp. 101-127
2. Džmuráňová H., Teplý P. (2014), Risk management of saving accounts, IES Working paper 09/2014
3. Gambacorta L. and van Rixtel A. (2013), Structural bank regulation initiatives: approaches and implications, BIS Working Papers No 412 – Monetary and Economic Department.
4. Hahm J.H. (2008). Determinants and Consequences of Non-Interest Income Diversification of Commercial Banks in OECD Countries, Journal of International Economic Studies, Vol. 12, No. 1.
5. Heffernan S. A. and Fu M. (2010), Determinants of Financial Performance in Chinese Banking, Applied Financial Economics, Vol. 20 (20), pp. 1585–1600.
6. Kim J. G. and Kim Y. J. (2010), Noninterest income and financial performance at South Korea banks
7. Köhler M. (2013). Does non-interest income make banks more risky? Retail- versus investment-oriented banks, Deutsche Bundesbank, Discussion Paper No17
8. Roodman D. (2006), How to Do xtabond2: An Introduction to “Difference” and “System” GMM in Stata, Working Paper, No. 103, Centre for Global Development.
9. Růžičková K., Teplý P. (2015), Determinants of Banking Fee Income in the EU Banking Industry – Does Market Concentration Matter? IES Working paper 04/2015
10. Wooldridge J.M. (2002), Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge.
|Předběžná náplň práce v anglickém jazyce|
Nowadays, charging of fees for services is highly discussed topic from both customer’s and bank’s view. Whereas customers usually perceive them as unnecessary and excessively high extra money paid, firms introduce them for compensation of their costs of services and consider them as a tool for continuous improving of the quality of those services. Therefore, fees also play an important role in the financial market competition. As a consequence, non-interest income has been becoming a very important part of the banks’ income. Moreover, after severe losses of banks experienced during the crisis, the non-interest income has increased even more on its importance.
The aim of this thesis is to analyze the volatility of interest income and non-interest (fee) income across the EU banks and banks of U.S. and the main task will be to find out the potential relation between bank’s fees and bank’s performance in terms of profitability and risk.
The analysis of this topic will be mainly provided on the macroeconomic level. The empirical part will analyze the riskiness of European and American banks arising from the higher non-interest income. Due to large differences among both the EU banks and among the American and European banking market (e.g. in terms of size or health), appropriate methodology will be used to show this fragmentation.
1. Hypothesis #1: Prepayment risk is higher in low interest rate periods.
2. Hypothesis #2: Non-interest income increases the riskiness of all types of banks.
3. Hypothesis #3: Fee income before the crisis was less volatile than during and after the financial crisis in 2008-2010.
The analysis will be primarily based on balanced panel data from EU countries published in Bank Scope database. Other suitable data will be taken from ECB database, the World Bank, Eurostat and individual national banks. Firstly, some simple descriptive statistics will be used for comparison of gained data scope among EU and U.S. countries. The core methodology will then be the System GMM which enables to estimate properly the dynamic panel data models with possible undesirable effects present in data (e.g. endogeneity). Afterwards, for the check of data robustness, fixed effects, random effects and pooled OLS will be run.
We will try to find the presence of direct effect of non-interest income on bank’s performance. Unlike the majority of the current studies to related topics, we will use system GMM as the most suitable methodology and thus we will deal with the endogeneity problem better. Further, our dataset will not be focused only on single country or on countries of EU, but we will combine European and American countries and therefore will eventually be able to study different conditions occurring in U.S. and EU bank market.
2. Theoretical part
2.1. Basic terms (consumer protection, risk management, yield curves)
2.2. Banking industry and types of banks in the EU and U.S.
3. Empirical part
3.1. Literature review
3.2. Data analysis (BankScope)
3.4. Results and key findings