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We develop a theory of endogenous uncertainty where the ability of investors to learn about firm-level fundamentals declines during financial crises. At the same time, higher uncertainty reinforces financial distress of firms, giving rise to "belief traps" -a persistent cycle of uncertainty, pessimistic expectations, and financial constraints, through which a temporary shortage of funds can develop into a long-lasting funding problem for firms. At the macro-level, belief traps can explain why financial crises can result in long-lasting recessions. In our model, financial crises are characterized by high levels of credit misallocation, an increased cross-sectional dispersion of growth rates, endogenously increased pessimism, uncertainty and disagreement among investors, highly volatile asset prices, and high risk premia. A calibration of our model to U.S. micro data on investor beliefs explains a considerable fraction of the slow recovery after the 08/09 crisis.

The integration of renewable energy sources (RES) into local energy distribution networks becomes increasingly important. Renewable energy highly depends on weather conditions, making it difficult to maintain stability in such networks. To still enable efficient planning and balancing, forecasts of energy supply are essential. However, typical distribution networks contain a variety of heterogeneous RES installations (e.g. wind, solar, water), each providing different characteristics and weather dependencies. Additionally, advanced meters, which allow the communication of finalgranular production curves to the network operator, are not available at all RES sites. Despite these heterogeneities and missing measurements, reliable forecasts over the whole local distribution network have to be provided. This poses high challenges on choosing the right input parameters, statistical models and forecasting granularity (e.g. single RES installations vs. aggregated data). In this paper, we will discuss such problems in energy supply forecasting using a real-world scenario. Subsequently, we introduce our idea of a generalized optimization approach that determines the best forecasting strategy for a given scenario and sketch research challenges we are planning to investigate in future work.

We explore how pricing dynamics in the European airline industry vary with the competitive environment. Our results highlight substantial variations in pricing dynamics that are consistent with a theory of intertemporal price discrimination. First, the rate at which prices increase towards the scheduled travel date is decreasing in competition, supporting the idea that competition restrains the ability of airlines to price-discriminate.Second, the sensitivity to competition is substantially increasing in the heterogeneity of the customer base, reflecting further that restraints on price discrimination are only relevant if there is initial scope for price discrimination. These patterns are quantitatively important, explaining about 83 percent of the total within-flight price dispersion, and explaining 17 percent of the observed cross-market variation of pricing dynamics.

Many important statistics in macroeconomics and finance-such as cross-sectional dispersions, risk, volatility, or uncertainty-are second moments. In this paper, we explore a mechanism by which second moments naturally and endogenously fluctuate over time as nonlinear transformations of fundamentals. Specifically, we provide general results that characterize second moments of transformed random variables when the underlying fundamentals are subject to distributional shifts that affect their means, but not their variances. We illustrate the usefulness of our results with a series of applications to (1) the cyclicality of the cross-sectional dispersions of macroeconomic variables, (2) the dispersion of MRPKs, (3) security pricing, and (4) endogenous uncertainty in Bayesian inference problems.