Research & Projects

Current workingpapers

Thomas Kruse, Judith C. Schneider, Nikolaus Schweizer: A toolkit for robust risk assessment using F-divergences, Revise and Resubmit: Management Science, 2019.
This paper assembles a toolkit for the assessment of model risk when model uncertainty sets are defined in terms of an F-divergence ball around a reference model. We propose a new family of F-divergences that are easy to implement and flexible enough to imply convincing uncertainty sets for broad classes of reference models. We use our theoretical results to construct concrete examples of divergences which allow for significant amounts of uncertainty about lognormal or heavy-tailed Weibull reference models without implying that the worst case is necessarily infinitely bad. We implement our tools for two problems from financial risk management.

Hannes Mohrschladt, Judith C. Schneider: The idiosyncratic volatility puzzle and its interplay with sophisticated and private investors, Revise and Resubmit: Review of Derivatives Research, 2019.
We establish a direct link between sophisticated investors in the option market, private stock market investors, and the idiosyncratic volatility (IVol) puzzle. To do so, we employ three option-based volatility spreads and attention data from Google Trends. In line with the IVol puzzle, the volatility spreads indicate that sophisticated investors indeed consider high-IVol stocks as being overvalued. Moreover, the option measures help to distinguish overpriced from fairly priced high-IVol stocks. Thus, these measures are able to predict the IVol puzzle’s magnitude in the cross-section of stock returns. Further, we link the origin of the IVol puzzle to the trading activity of irrational private investors as the return predictability only exists among stocks that receive a high level of private investor attention. Overall, our joint examination of option and stock markets sheds light on
the behavior of different investor groups and their contribution to the IVol puzzle. Thereby, our analyses support the intuitive idea that noise trading leads to mispricing, which is identified by sophisticated investors and exploited in the option market.

Hannes Mohrschladt, Judith C. Schneider: Option-Implied Skewness: Insights from ITM-Options, submitted, 2020.
While the standard to calculate model-free option-implied skewness (MFIS) relies on out-of-the-money (OTM) options, we examine the empirical implications of using in-the-money (ITM) options. First, we show that discarding ITM-options based on liquidity arguments appears unjustifiable for individual stock options. Second, the information content of ITM-options provides new economic insights. In particular, we find that the positive short-term return predictability of OTM-based MFIS significantly reverses if ITM-options are used instead. While this reversal is inconsistent with an explanation based on skewness preferences, MFIS apparently reflects information that is not timely incorporated in stock prices due to market frictions. Based on these insights, we introduce ∆MFIS as a new measure of additional option-embedded information that significantly predicts subsequent returns beyond a
large range of other option-based return predictors.

Devdeepta Bose, Colin Camerer, Henning Cordes, Sven Nolte, Judith C. Schneider: Decision weights for experimental asset prices based on visual salience, 2020 (R&R Review of Financial Studies).
Using a machine-learning algorithm that can predict visually salient portions of images, we construct decision weights based on salient parts of a stock price chart. We analyze these weights in three experimental studies that vary in the realism of the price path images and task complexity. We find that these decision weights are predictive of future invest- ments. We conclude that visual salience captures attention paid to historical returns. Visual salience goes beyond overweighting returns at the tails of the historical distribution or with respect to their difference to a reference return as in the models of Barberis et al. (2016) and Bordalo et al. (2013). Moreover, we find that visual salience affects investment decisions independently from recency effects.