Cross hedging price risk in an incomplete financial market creates basis risk. We propose a new way of modeling basis risk where price risk and basis risk are combined in a multiplicative way. Under this specification, positive prudence is a necessary and sufficient condition for underhedging in an unbiased market. Using the example of cross hedging jet fuel price risk with crude oil futures, we show that the new specification is superior in describing the price series and that optimal cross hedges differ significantly from those derived under the traditional additive cross hedging model.

We use a cross-section of economic survey forecasts to predict the distribution of US macro variables in real time. This generalizes the existing literature, which uses disagreement (i.e., the cross-sectional variance of survey forecasts) to predict uncertainty (i.e., the conditional variance of future macroeconomic quantities). Our results show that cross-sectional information can be helpful for distribution forecasting, but this information needs to be modeled in a statistically efficient way in order to avoid overfitting. A simple one-parameter model which exploits time variation in the cross-section of survey point forecasts is found to perform well in practice.

In a factor timing context, academic research has focused on identifying a set of predictors that can explain the dynamics of factor portfolios. We propose an alternative approach for timing factor portfolio returns by exploiting the information from their portfolio characteristics. Different combinations of dimension reduction techniques are employed to independently reduce the number of both predictors and portfolios to predict. Characteristic-based models outperform existing methods in terms of exact predictability, as well as investment performance. (JEL G10, G11, C52, C55)

We propose a new approach to examine sell-side analysts’ career concerns by relating their forecast boldness to their employers’ news flows. Specifically, we use banking sector news to proxy for the severity of career concerns. Analysts follow more closely the consensus forecast when the prospects of the banking sector are negative (and vice versa). The effect is both economically and statistically significant after controlling for various firm, analyst, brokerage house, and forecasting characteristics, as well as sector and economy wide effects. The more established analysts, in terms of reputation and experience, are generally unaffected by banking sector news. In contrast, their less established peers tend to cluster their forecasts near the consensus after a sequence of negative news flows for banks. Collectively, our results support the notion that during banking stresses when job security is low analysts’ tendency to imitate others increases.

It is well documented that since at least the 1970s investment-cash flow (I-CF) sensitivity has been decreasing over time to disappear almost completely by the late 2000s. Based on a neoclassical investment model with costly external financing, we show that this pattern can be explained by the gradual increase of capital adjustment costs, attributable to the accumulation of knowledge capital. The result is robust to a variety of approaches, including Euler equation estimation and the simulated method of moments. More generally, our findings demonstrate that I-CF sensitivity should only be interpreted as a joint measure of financial and real frictions.

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.