Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics

We present a framework to identify market responses to uncertainty faced by firms regarding both the potential incidence of extreme weather events and subsequent economic impact. Stock options of firms with establishments in forecast and realized hurricane landfall regions exhibit large increases in implied volatility, reflecting significant incidence uncertainty and long-lasting impact uncertainty. Comparing ex ante expected volatility to ex post realized volatility by analyzing volatility risk premia changes shows that investors significantly underestimate extreme weather uncertainty. After Hurricane Sandy, this underreaction diminishes and, consistent with Merton (1987), these increases in idiosyncratic volatility are associated with positive expected stock returns.

Assessing Transparency, Accuracy, and Consistency of Relative Importance of Cow-Calf Profitability Drivers Using Neural Networks versus Regression

Using both multivariate regression and artificial neural networks, the relative impact of variables affecting cow-calf profitability was examined over two cattle cycles for spring- and fall-calving herds that varied in size by time period analyzed when using different fertility management affecting forage yields with and without weather uncertainty. Neural networks had greater predictive accuracy than regression but at the cost of lesser transparency and predictive consistency. Explaining profitability, price, and quantity of cattle sold were consistently and respectively ranked first and second using both approaches. Importance rankings for hay sold and fertilizer were low and less consistent across techniques employed.

UAV Mission Planning Resistant to Weather Uncertainty

Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.

Weather Uncertainty and Efficacy of Fumigation in Tomato Production

This study quantifies the effect of weather factors on fumigation efficacy in terms of weed control, tomato yield, and the overall economic performance of fumigants. High soil temperature was found to reduce the efficacy of all fumigants against nutsedge, while rainfall only reduced the efficacy of a limited number of fumigants. The fumigants’ economic performance over a range of weather conditions was further simulated to identify the fumigant that is most effective under diverse weather conditions. The results show that although 1,3-D:Pic:Kpam outperforms methyl bromide over the experiment period, methyl bromide is still the best treatment when accounting for the impact of weather variability. The study illustrates the sensitivity of fumigant efficacy to weather conditions and the importance of achieving consistent and sustainable efficacy. The regression model and the expected utility model, along with the simulation techniques, form a useful tool that can be applied across regions or crops.