J
Joshua D. Woodard
Researcher at Cornell University
Publications - 50
Citations - 832
Joshua D. Woodard is an academic researcher from Cornell University. The author has contributed to research in topics: Crop insurance & Futures contract. The author has an hindex of 15, co-authored 50 publications receiving 753 citations. Previous affiliations of Joshua D. Woodard include Texas A&M University & University of Illinois at Urbana–Champaign.
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Journal ArticleDOI
Basis risk and weather hedging effectiveness
Joshua D. Woodard,Philip Garcia +1 more
TL;DR: In this article, the authors investigate several dimensions of weather basis risk in the U.S. corn market and suggest that while geographic basis risk can be significant, it should not preclude the use of geographic cross-hedging, particularly with temperature as opposed to precipitation derivatives.
Journal ArticleDOI
A Spatial Econometric Analysis of Loss Experience in the U.S. Crop Insurance Program
TL;DR: In this paper, a spatial econometric model of the U.S. corn insurance market is used to estimate cross-subsidizations across the primary corn-producing states and counties.
Posted ContentDOI
Weather Derivatives, Spatial Aggregation, and Systemic Risk: Implications for Reinsurance Hedging
Joshua D. Woodard,Philip Garcia +1 more
TL;DR: The authors found that better weather hedging opportunities may exist at higher levels of spatial aggregation, which suggests that the potential for weather derivatives in agriculture may be greater than previously thought, particularly for aggregators of risk such as reinsurers.
Journal ArticleDOI
Historical extension of operational NDVI products for livestock insurance in Kenya
Anton Vrieling,Michele Meroni,Apurba Shee,Andrew G. Mude,Joshua D. Woodard,C.A.J.M. de Bie,Felix Rembold +6 more
TL;DR: Good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes, and it was shown that this extension may have large effects on the calculated insurance premium.
Journal ArticleDOI
Estimation of Mixture Models Using Cross-Validation Optimization: Implications for Crop Yield Distribution Modeling
TL;DR: In this paper, the authors provide a methodology for estimating flexible and efficient mixture models using cross-validation that alleviates many of these associated model selection issues, and demonstrate that nonparametric models often fit best in-sample but are inefficient and consistently overstate true rates.