Together with Sinan Krückeberg, my Ph.D. student, we have worked on a paper on cryptocurrencies and have analyzed if they form an asset class on their own. You find our interesting results in our working paper.
Cryptocurrencies show characteristics of a distinct asset class based on strong internal correlation, an absence of correlation with any traditional asset class as well as strong market liquidity, while market stability has room for improvement. We find that for investment purposes cryptocurrencies can be distinguished into cryptographic coins and tokens. Adding a 1% allocation of cryptocurrencies to traditional portfolio structures leads to significant and persistent risk weighted outperformance. These results support the careful introduction of cryptocurrencies into the asset management mainstream.
Sinan Krückeberg & Peter Scholz (2018): Cryptocurrencies as an Asset Class? SSRN Working Paper.
Together with my Ph.D. student David, we have analyzed liquidity risks and therefore finished the third article for his thesis.
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.
David Großmann & Peter Scholz (2017): The Golden Rule of Banking: Funding Cost Risks of Bank Business Models. SSRN Working Paper.
In a joint work with Michael Tertilt, an HSBA alumnus of MBA Corporate Management, we analyze the current quality of robo-advice.
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.
Michael Tertilt & Peter Scholz (2017): To Advise, or Not to Advise — How Robo-Advisors Evaluate the Risk Preferences of Private Investors. SSRN Working Paper.