Working Papers
Working Papers
Do Capital Requirements Make Banks Safer? Evidence From a Quasi-Natural Experiment
Denefa Bostandzic, Felix Irresberger, Ragnar Juelsrud, and Gregor Weiß
R&R Journal of Financial and Quantitative Analysis
2020
Abstract: We use the EBA capital exercise of 2011 as a quasi-natural experiment to investigate how capital requirements affect various measures of bank solvency risk. We show that, while regulatory measures of solvency improve, non-regulatory measures indicate a deterioration in bank solvency in response to higher capital requirements. The decline in bank solvency is driven by a permanent reduction in banks' market value of equity. This finding is consistent with a reduction in bank profitability, rather than a repricing of bank equity due to a reduction of implicit and explicit too-big-too-fail guarantees. We then discuss alternative policies to improve bank solvency.
Financial Technology And Local Lending
Denefa Bostandzic, and Gregor Weiß
2020
Abstract: We study the effects of innovations in financial technology by banks on local competition for deposits and credit supply. To identify the causal effect of financial technology on deposits and lending, we exploit the geographic heterogeneity in human capital available to bank headquarters to explain banks' patenting activities. Banks that innovate increase their local market power by gaining deposits in a zero-sum game at the expense of local non-innovating competitors. Innovative banks make use of both the additional liquidity as well as process innovations themselves and expand aggregate local mortgage and small business lending without impairing the quality of their loan portfolio. Finally, we show that the innovation-induced credit supply shock spurs local economic growth and employment.
Estimating Implied Volatilities Via Machine Learning And The Cross-Section Of Option Returns
Simon Fritzsch, and Gregor Weiß
Work in Progress
2020
Abstract: We estimate conditional quantile curves of implied stock option volatilities using machine learning. We employ a new leveraged optimal quantization algorithm to quantify volatility mispricing in put options written on the constituents of the S&P 100. We find that a zero-cost trading strategy that is long (short) in the portfolio with low (high) implied volatility conditional on the options’ moneyness and historical volatility produces an economically and statistically significant average monthly return. Using conditional quantile curves not only helps in distinguishing volatility mispricing from other effects, it also leads to returns that are higher than those reported in previous work on similar volatility strategies.