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Papers from 4th International Agricultural Risk, Finance and Insurance Conference (IARFIC), Washington DC June 16-18th 2015 organised by Lysa Porth and Ken Seng Tang

Papers from 4th International Agricultural Risk, Finance and Insurance Conference (IARFIC), Washington DC June 16-18th 2015 organised by Lysa Porth and Ken Seng Tang

The International Agricultural Risk, Finance, and Insurance Conference (IARFIC) aims to facilitate important and timely discussions regarding agricultural risk management and insurance, through bringing together leaders across the globe and across disciplines, including agricultural economics, actuarial science, finance, statistics, and risk management. IARFIC serves as an important platform for the widespread and in depth dispersion of novel work and ideas. Collaboration is essential for maximizing the potential of innovative scientific research in addressing global challenges. The collaboration of academia, government, the private sector, NGO's, and other stakeholders ensures that critical up to date information is received from and sent through important channels towards improving food security.


Adoption and impact of index-insurance and credit for smallholder farmers in developing countries: a systematic review,An autoregressive approach to modeling commodity prices as a quasi-fractional Brownian motion,Big data and ag-analytics: an open source, open data platform for agricultural & environmental finance, insurance, and risk,Building insurance through an NGO: approaches and experiences from a rice insurance pilot in Haiti,Editorial,Financial engineering in pricing agricultural derivatives based on demand and volatility,Hedging crop yield with exchange-traded weather derivatives,Price and volatility transmissions between natural gas, fertilizer, and corn markets,Quantitative finance for agricultural commodities: discussion and extension,The risk and return of farm real estate and the “bad data problem”,The US crop insurance program and WTO disciplines,Time scale and fractionality in financial time series

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