Are the German Banks Riskier than the European Competitors - Essay Example

Are the German banks riskier than the European competitors? Germany had witnessed a more crucial phase than did its other European counterparts, which is why the need arises to implement certain anti-risk policy measures. The present paper tries to explore the fact that the German banks have in the process been successful in implementing anti-risk measures in their policies in comparison to their European peers. Use of statistical software STATA has been made to draw a comparative analysis. Table of Contents Chapter 1 - Introduction 4 Chapter 2 – Literature Review and Theoretical Background 4 Chapter 3 - Data Collection and Methodology 5 Chapter 4 – Results and Discussions 8 Chapter 5 – Conclusion and Recommendations 10 Reference 11 Robbins, G. S. (2000) Banking in transition: East Germany after unification New York: Macmillan Press Limited 11 Bibliography 11 Carson, R. L. (1990) Comparative Economic Systems (Part III: Capitalist Alternatives) New York: M. E. Sharpe. 11 Green, W. H. (1997) Econometric Analysis (3rd Edition) London: Prentice Hall. 11 Groppelli, A. A. & Nikhbakht, E. (2000) Finance (4th Edition) New York: Barrons. 11 Ross, S. A., Westerfield, R. & Jaffe, J. (2004) Corporate Finance New York: McGraw-Hill. 11 Appendix 12 Chapter 1 - Introduction There had been a number of arguments revolving around the risk element present in the German banks. After the sub prime crisis had surged the globe and the banks around the world had been indulged in a rather crucial period, a number of policy measures had been adopted by banks all around Europe. Even before 2005, there had been another crucial phase of global depression – when the East Asian crisis had surfaced in 1997. Both the periods proved to be rather critical for the banks around the world. However, it has often been argued that despite being on the same ship, the German banks had always been able to evolve successfully out of the tragedy. The present paper performs an empirical analysis of the comparative risk elements present in the European and the German banks. Chapter 2 – Literature Review and Theoretical Background There are a number of reasons why the German banks should be so eager to implement risk anti-risk policies. As Robbins had put it (Robbins, 2000), after the unification of East and West Germany, the banks in the united nation needed to win the trust and the faith in the minds of the people, so that they had to undertake some relevant and careful anti-risk policy measures. The policy measures have continued ever since; even a few days back, the Chairman of Deutsche Bank, Joseph Ackerman, had reportedly said that there would be a worldwide availability of German bank shares in order to minimize the risk elements present in the business. Theoretical Background In order to know about the relative risk quotient of the European and the German banks, an analysis is made of certain ratios at this point. The ratios being considered are described as follows. 1. ETA = Equity / Total Assets (%) 2. NIM = Net Interest Margin (%) 3. ROA = Return on Average Assets (ROAA) (%) 4. CI = Cost to Income Ratio (%) 5. loanasset = Net Loans / Total Assets (%) 6. loandeposit = Net Loans / Customer & ST Funding (%) 7. llpintrev = Loan Loss Provision / Net Interest Revenue (%) 8. netintrevta = Net Interest Revenue / Average Assets (%) 9. oopincta = 0th Opportunity Income / Average Assets (%) 10. nintexpta = Non Interest Expenses / Average Assets (%) 11. llpl = Loan Loss Provisions / Gross Loans (%) 12. liquid = Liquid Assets / Total Assets (%) Since the mean or the average values are irrelevant to the study and rather the standard deviations are the ones that are needed for the same, so further description of the ratios have been omitted. The objective is to find out the relative risk quotient of the variables corresponding to the German and other European banks. The one that is found to display a higher standard deviation of returns is considered to be the riskier one. In order to ensure that the study involves the banks which operate in the market at present, only the active banks or the ones which have been merged with other banks have been taken into account. Chapter 3 - Data Collection and Methodology Data Collection In order to carry on with the above specified empirical analysis, data has been collected on all banks located within the Euro region. In addition, data for the German banks have been categorized separately, so as to carry on the comparison with that of their European counterparts. The information being collected is annual in nature and has been collected for a period of ten years between 1997 and 2008. It comprises of various relevant variables, but the ones that are suitable for the measurement of risk of the banking sectors in the two regions are not raw data, but rather ratios of some significant raw data, which have already been mentioned in the previous section of the paper. Methodology However, in order to conduct a comparative risk analysis between the European and the German banks, it is essential to conduct some statistical tests. A universally accepted method of risk analysis is through the computation of the standard deviation or the variance of the observational values. This is because standard deviation helps to measure the variations in the values that the variables take. The higher the variations in the values are, higher is the dispersion in the values that a variable will probably assume. So, higher the value of the standard deviation or the variance of the returns from a variable, or in the values that it presumes is, higher will be the risk associated with the variable. To compare between two such similar values in two different cases, though might not provide a truthful outcome, given the fact that the average values of the same variable for two different datasets might vary, that does not bring them at par with one another, only through a simple comparison of their standard deviation values. This is the reason why it is important to apply some statistical measure able to effectively compare between the standard deviation values of the same variable for two different datasets, so as to decide about the riskier dataset out of the two. But before designing any appropriate measure, it is important to frame the relevant hypothesis first. Hypothesis The hypothesis to test for the riskiness in the observations of one variable than that for its counterpart in another dataset is, H0: Standard deviation for dataset ‘1’ – Standard Deviation for dataset ‘2’ = 0 against H1: Standard deviation for dataset ‘1’ – Standard Deviation for dataset ‘2’ > 0 The alternative hypothesis in this case is of a greater than type so that the hypothesis testing will mainly be of a greater-than type. Hypothesis Testing The statistical test most appropriate for hypothesis testing in this case will be, F-test, defined as under – F = (s1/ n1)/ (s2/ n2) Where, s1, s2 are the standard deviations of the observations for datasets ‘1’ and ‘2’ respectively. Here, si = √[(∑Xi - X)2 / ni]; i = 1, 2 Where, n1, n2 are the number of observations in datasets ‘1’ and ‘2’ respectively. Here, n1, n2 are the degrees of freedom of the estimated F statistic. The benchmark rules to accept or reject the null hypothesis are presented underneath. If the level of significance has been assumed to be 0.05, i.e., 5%, then1, 1. If Estimated F > Tabulated F, reject the null hypothesis at 5% level of significance and 2. If Estimated F < Tabulated F, do not reject the null hypothesis at 5% level of significance. A comparison between the risk factor of the European and German bank variables have been made with the help of the statistical software STATA, that examines the probability of three cases, namely, when critical F is greater than estimated F, when critical F is equal to estimated F and when critical F is less than estimated F. the probability that yields the greatest value of the three should be accepted as the true case. Chapter 4 – Results and Discussions There are in all 12 relevant variables for each of the bank in Germany as well as those exogenous to the German economy, but a part of the Euro zone. All these variables have been compared between either of the two datasets with the help of the aforesaid procedures. The descriptive statistic details for all these variables have been presented in the appendix to the chapter. After the descriptive statistics have been obtained, the next step is to compare between the standard deviation of each pair of observations so as to find the relative risk element present of the two. The relevant estimated F-statistics along with their degrees of freedom and the corresponding critical F-values were attained, that have been presented in the following table.2 Table 1 – Estimated F-statistics Variable Estimated F Degrees of freedom ETA 2.5166 17505, 14100 nim 0.8667 17233, 14046 ROA 3.8816 17457, 14049 CI 2.4124 17006, 14040 loanasset 2.1261 17282, 14058 loandeposit 1.4540 17083, 13992 llpintrev 1.1232 15956, 13784 oopincta 2.8352 17231, 14048 nintexpta 2.1482 17199, 14047 llpl 0.0799 15820, 13720 liquid 2.6065 17435, 14097 Data Analysis The results of hypothesis testing have been obtained with the help of the statistical software STATA, and as already specified in the previous section, test for the case at which the probability is the highest and accepts that one as the actual case, with a confidence equal to (1 – α)3. The status of the comparison between estimated and tabulated F-statistics for different variables has been presented in the following table – Table 2 – Probability of Estimated versus Tabulated F-statistics Variable P (F > f) P (F = f) P(F < f)4 ETA 0.0000 0.0000 1.0000 nim 1.0000 0.0000 0.0000 ROA 0.0000 0.0000 1.0000 CI 0.0000 0.0000 1.0000 loanasset 0.0000 0.0000 1.0000 loandeposit 0.0000 0.0000 1.0000 llpintrev 0.0000 0.0000 1.0000 oopincta 0.0000 0.0000 1.0000 nintexpta 0.0000 0.0000 1.0000 llpl 1.0000 0.0000 0.0000 liquid 0.0000 0.0000 1.0000 * The actual cases have been emboldened and italicized. In most of the cases it is found that the probability of the calculated F statistic being greater than the tabulated one is 1, implying that it is going to be the case for the given sample of observations. Since the ratio for the calculated F consists of the standard deviation in case of European banks in the numerator and that for the German banks in the denominator, for any particular variable, the implication is quite obvious. The probabilities captured in Table 2 above imply that the standard deviation for a particular variable returns is higher in case of European banks than for the German banks, so that the former could be said to be the riskier one of the two after a comparative analysis is being made. Chapter 5 – Conclusion and Recommendations The German banks are empirically found to be less risky than their European peers, from a large number of aspects. Thus the empirical research analysis being conducted in the present paper is in line with what the reality says (discussed in the Introduction to the paper). In fact, the lower variations in the returns of the German banks indicate that the policies that have been implemented by the Deutsche bank have rather worked wonders for the economy, which has posited it at a higher rank than many of its European counterparts. However, one drawback of the study is that, data for all the banks have not been included due to unavailability. Moreover, the study has been restricted to the active banks in Europe, rather than including even the dissolved ones. But the argument that could be raised in this context is that, since the study concerns the case for the current period, so dissolved, liquidated or bankrupted banks have nothing to do with it. Again, though, the study could have led to a stronger conclusion had all the data been included in the paper, it couldn’t have varied much with the present outcome. In fact, an examination after the inclusion of all relevant data could provide for a further research in the area. Reference Robbins, G. S. (2000) Banking in transition: East Germany after unification New York: Macmillan Press Limited Bibliography Carson, R. L. (1990) Comparative Economic Systems (Part III: Capitalist Alternatives) New York: M. E. Sharpe. Green, W. H. (1997) Econometric Analysis (3rd Edition) London: Prentice Hall. Groppelli, A. A. & Nikhbakht, E. (2000) Finance (4th Edition) New York: Barrons. Mentre, P. (1984) The fund, commercial banks and member countries Washington, D. C.: International Monetary Fund Ross, S. A., Westerfield, R. & Jaffe, J. (2004) Corporate Finance New York: McGraw-Hill. Appendix Table A (a) Descriptive Statistics of ratios for German banks Variable Mean Standard Deviation ETA 6.65154 8.646182 nim 2.902859 6.638449 ROA .3083075 1.702453 CI 69.65981 21.37136 loanasset 59.77368 16.29676 loandeposit 73.47985 50.19202 llpintrev 19.13575 34.89846 oopincta 1.422571 6.095778 nintexpta 3.432437 5.953062 llpl 1.812371 26.3376 liquid 15.33691 13.36056 Table A (b) Descriptive Statistics of ratios for European banks Variable Mean Standard Deviation ETA 11.61376 13.71625 nim 2.939836 6.180168 ROA .9074144 3.354155 CI 67.38564 33.1935 loanasset 50.72886 23.76254 loandeposit 76.85907 60.52253 llpintrev 12.76016 36.98549 oopincta 2.030375 10.26418 nintexpta 3.558323 8.72525 llpl .7241491 7.443798 liquid 30.94894 21.57009 Read More