The Golden Rule of Banking: Funding Cost Risks of Bank Business Models

 

Together with my Ph.D. student David, we have analyzed liquidity risks and therefore finished the third article for his thesis.

Abstract

The liquidity regulation of banks in Pillar 1 of the Basel framework does not consider funding cost risks of different bank business models. Therefore, we assemble a data set of balance sheet positions including maturities and use the method of Value-Liquidity-at-Risk to explore 118 European retail, wholesale, and trading banks. When examining liquidity-induced equity risks, trigged by exemplary rating shifts, we find that retail banks bear significantly lower funding cost risks than wholesale and trading banks. Consequently, a prudential regulation, which simultaneously considers the funding cost risk and the diversification of the banking system is recommended.

Citation

David Großmann & Peter Scholz (2017): The Golden Rule of Banking: Funding Cost Risks of Bank Business Models. SSRN Working Paper.

PC-Lab: Computational Finance, M.Sc.

This module has two main goals: it serves as a repetition of content students should already be familiar with as well as an application by transferring formulas into action. We rely on Excel, VBA, and MATLAB as software packages and cover basic concepts from B.Sc. studies as well as CFA Level I content. First and foremost, the essential time value of money, followed by applications in risk management as well as asset pricing, e.g. bonds, derivatives, and portfolios. By the end of this course, the participants are able to remember and to understand these basic concepts and they learned to apply these concepts on real data. Weiterlesen

To Advise, or Not to Advise — How Robo-Advisors Evaluate the Risk Preferences of Private Investors

In a joint work with Michael Tertilt, an HSBA alumnus of MBA Corporate Management, we analyze the current quality of robo-advice.

Abstract

Robo-advisors promise efficient, rational, and transparent investment advisory. We analyze how robo-advisors ascertain their user’s risk tolerance and which equity exposure is derived from the individual risk profile. Our findings indicate significant differences in the quality of offered investment advice. On average, robo-advisors ask relatively few questions in their user’s risk profile assessment, and it is particularly surprising that some of the questions seem not to have any impact on the risk categorization. Moreover, the recommended equity exposure is relatively conservative.

Citation

Michael Tertilt & Peter Scholz (2017): To Advise, or Not to Advise — How Robo-Advisors Evaluate the Risk Preferences of Private Investors. SSRN Working Paper.

Better The Devil You Know Than The Devil You Don’t — Financial Crises between Ambiguity Aversion and Selective Perception

Together with my Ph.D. students David and Sinan, we have written an article for the second edition of the International Conference on European Integration and Sustainable Development.

Abstract

During financial crises, market participants are pressurized and presumably prone to emotional biased decisions. We use the Economic Policy Uncertainty Indicator and Dow Jones Industrial Average as well as Nikkei 225 GARCH volatilities to test for ambiguity aversion and selective perception of investors. For most crises, we find a significant link between uncertainty and market volatility. However, with respect to ambiguity aversion, the causality differs between crises indicating that investors may not always be driven by uncertainty. Regarding selective perception, we find significant results for the Dot.Com and subprime crises, but not for the Japanese asset price bubble and the Asian crisis.

Citation

Peter Scholz, David Großmann & Sinan Krückeberg (2017): Better The Devil You Know Than The Devil You Don’t — Financial Crises between Ambiguity Aversion and Selective Perception. SSRN Working Paper.

Bank Regulation — One Size Does Not Fit All

Abstract

Bank business models show diverse risk characteristics, but these differences are not sufficiently considered in Pillar 1 of the regulatory framework. Even if the business model is analyzed within the European SREP, global Pillar 2 approaches differ and could lead to competitive disadvantages. Using the framework of Miles et al. (2012), we examine a dataset of 115 European banks which is split into retail, wholesale, and trading banks. We show that shifts in funding structure affect business models differently. Consequently, a “one size” approach in Pillar 1 for the regulation of banks does not fit all.

Citation

David Großmann & Peter Scholz (2017): Bank Regulation — One Size Does Not Fit All. Journal of Applied Finance & Banking (7)5, 1-27.

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