International Banking Quantitative Study
Introduction
The purpose of this quantitative study is to assess the confidence levels of members of the international banking community with respect to the sector’s ability to weather another global economic crisis like that seen from 2007-2009 following the collapse of sub-prime in the U.S. and a tidal wave of leveraged defaults across the global banking sector which only found relief through central banking intervention (Haitsma, Unalmis & de Haan, 2016; Heller, 2017). To better understand the extent to which the international banking sector is prepared for another possible global economic crisis, it is helpful to address the community directly and obtain from the horse’s mouth, so to speak, how vulnerable real life bankers feel in 2018, what with a trade war between the U.S. and China possibly turning into a hot war, and numerous countries around the world audibly voicing their desire to begin getting away from the USD as a result of too many economic sanctions flowing out of Washington in recent years.
Statement of the Problem
The problem this study aims to address is to answer whether the international banking community feels confident that it can weather another global economic crisis, and specifically whether geo-political awareness impacts that confidence level. Confidence plays a large role in how money is invested, where it is placed, and what markets will do. Everything from bonds to equities to precious metals and even blockchain is impacted by confidence. Banks must have some sense of their confidence levels should another economic crisis hit. If confidence is low, knowing that can give banks an opportunity to de-leverage and reduce risk in order to better survive an economic crisis.
Purpose of the Study
The purpose of this study is to assess the confidence of members of the international banking community with regard to whether the sector can safely handle another global economic crisis like that seen in 2007-2009 and whether geo-political awareness impacts that confidence level.
Power Analysis
The power curve is important because it tells at what point a test becomes biased as well as the probability of rejecting the null hypothesis. The curve will illustrate the power of the test in a monotonic line that increases as the probability of the rejection of the null hypothesis rises and the null hypothesis becomes more and more false. The illustration helps to show where the level of significance is (and if the curve dips below this level, it indicates some form of bias in the test).
The p-value refers to the level of significance. For example, if 95% of the students’ tests show that the students answered the questions on pigs incorrectly, it would indicate that there is a statistically significant problem with the class’s understanding of pigs. If the p value is greater than alpha (.05) there is no statistical significance. If it is less than alpha then the difference is statistically significant. Alpha is the margin of error that is permitted in at test and it is usually placed at 5%. This study aims to achieve statistical significance.
Research Question
The question posed for this study is: Can the international banking sector handle another global economic crisis like that seen from 2007-2009? The specific question is this: Does geo-political awareness impact how confident members of the international banking community feel about whether the sector can handle another global economic crisis like that seen from 2007-2009?
Null Hypothesis
Members of the international banking community with geo-political awareness do not feel confident that the sector can handle another global economic crisis like that seen from 2007-2009.
Alternative Hypothesis
Members of the international banking community with geo-political awareness do feel confident that the sector can handle another global economic crisis like that seen from 2007-2009.
Literature Review
In determining the relationship between perceptions of risk in the international banking community and how risk management is conducted, a number of researchers have posed different questions to help understand this relationship. With the Great Recession still fresh in the minds of many and the possibility of another, worse recession looming, understanding the degree to which international banks are integrated can help to develop a sense of whether defaults in one part of the world—say, Italy—will impact the entire international banking community. The study by Bouvatier and Delatte (2015) measures the extent to which international banks are integrated by examining a) “the asset side of banks to document the adjustment of their foreign claims across time,” b) bilateral positions, and c) “the current state of banking (dis)integration in different geographical areas” (p. 354). The main variable that the article examines, therefore, is international banking integration—but to measure this variable, a series of other variables needed to be measured, including countries’ GDP, geographical distance, common language, common border, whether or not they are members of the Economic Integration Agreement and the European Economic Area, along with bilateral flows and a number of other indexes, including the Chinn-Ito index and the legal structure and property rights index. Among the 14 reporting countries and 186 partner countries during the period 1999–2012, the researchers found “marked fragmentation” inside the euro area, while outside the euro area the international banking community has become even more integrated than before. In short, the integration of EU banks has reversed while the integration of banks throughout the rest of the world has progressed more dramatically than before the Great Recession.
The study is helpful for showing a divergence of trends between the euro area banks and the banks of the rest of the world. It represents a serious risk both banks inside the euro area and banks outside the euro area—but for different reasons. However, the study ends with more questions than answers. For instance, does the retrenchment in the euro area indicate that international banking has transferred to outside the euro area? If so, what tail risk does that impose on international banking should a default or collapse occur, whether in Italy or in China or in the U.S.?
The study...…the IV’s affect on the DV.
Method of Sampling
The snowball method of sampling will be used. An initial pool of participants will be obtained using the researcher’s connections on social media sites, including LinkedIn, Facebook and Twitter, which consists of over 700 contacts, more than half of which are members of the international banking community. This initial group will be sent the survey and will be requested to pass it on to one other banker they know.
Type of Data and How it will be Collected
The type of data collected will be responses to survey questions designed to measure bankers’ geo-political awareness and sense of geo-political significance to banking and their confidence levels regarding how well the banking sector can handle another economic crisis. The data will be collected using the survey method online, with participants completing a digital survey via one of the social media platforms.
Ethical Issues and Risk of Participation
Ethical issues include protecting the identity of participants and obtaining their informed consent. Consent will be assumed when the survey is completed and returned. Information about the survey will be provided at the top of the survey for the participant to read. Identifying information about the participant, such as name and the name of the bank the participant works for will not be requested or collected. This will preserve the anonymity of the participants. An internal review board will also be used to oversee the process.
How Data will be Analyzed
The parametric test is a hypothesis test that is used to obtain general information that can then be used to explain the mean of the sample. A typical parametric test is the t-test. Pearson’s correlation test is another typical parametric test. Essentially, whenever an assumption about the parameters of a sample population is utilized, the parametric test is the statistical test that the researcher will engage. This study will analyze the data using the t-test and Pearson’s correlation test.
How the Power Analysis was Conducted
Power analysis was conducted using Quick-R: “The significance level defaults to 0.05. Therefore, to calculate the significance level, given an effect size, sample size, and power, use the option ‘sig.level=NULL’” (Quick-R, 2018).
How Data will be Handled
Data will be handled by keeping all returned surveys in a digital locker that is encrypted. Only the researcher will have access to the digital locker.
Foreseeable Problems Implementing the Design
Support and budget may be two problems implementing the design. Time and scope are always obstacles in any research study, but for this study to be possible it requires budgeting time and obtaining support from bankers in the international banking community. It is possible that there is little interest from the community in responding to the survey.
Possible Prevention Methods
Connecting with individuals via social media ahead of time and getting the survey out to them early and to as many people as possible using the snowball method of sampling will help to prevent potential issues with these challenges.
References…
References
Bouvatier, V., & Delatte, A. L. (2015). Waves of international banking integration: A tale of regional differences. European Economic Review, 80, 354-373.
Bruno, V., & Shin, H. S. (2015). Capital flows and the risk-taking channel of monetary policy. Journal of Monetary Economics, 71, 119-132.
Chenail, R. J. (2011). Interviewing the investigator: Strategies for addressing instrumentation and researcher bias concerns in qualitative research. The Qualitative Report, 16(1), 255-262.
Haitsma, R., Unalmis, D., & de Haan, J. (2016). The impact of the ECB's conventional and unconventional monetary policies on stock markets. Journal of Macroeconomics, 48, 101-116.
Heller, R. (2017). Monetary mischief and the debt trap. Cato Journal, 37(2), 247-261.
Kanagaretnam, K., Lobo, G. J., Wang, C., & Whalen, D. J. (2015). Religiosity and risk- taking in international banking. Journal of Behavioral and Experimental Finance, 7, 42-59.
Weiß, G. N., Bostandzic, D., & Neumann, S. (2014). What factors drive systemic risk during international financial crises?. Journal of Banking & Finance, 41, 78-96.
Wong, E., Tsang, A., & Kong, S. (2015). International Banking and Liquidity Risk Transmission: Evidence from Hong Kong SAR. IMF Economic Review, 63(3), 515-541.
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