- Introduction to Quantitative Risk Management and Risk in Agricultural Business: Cutting Edge Quantitative Concepts and Methodologies -- Index-based Insurance Design for Climate and Weather Risk Management: A Review -- Weather and Yield Index-Based Insurance Schemes in the EU Agriculture: A Focus on the Agri-CAT Fund -- Avocado Production Index Insurance: An Application of Credibility Theory on Heterogeneous Data -- How do Economic Variables Affect the Pricing of Commodity Derivatives and Insurance? -- Empirical Results for Cross Hedging in the Incomplete Market -- Crop Yield Insurance Analysis for Turkey: Spatiotemporal Dependence -- Model and Forecast Combination for Predictive Yield Distributions in Crop Insurance -- A Recursive Method on Estimating ARFIMA in Agricultural Time Series -- Examining the Impact of Weather Factors on Agricultural Market Price Risk: An XAI Approach -- Textual Analysis in Agriculture Commodities Market -- Applications of Singular Spectrum Analysis in Agricultural Financial Time Series.

Open Access

This open access volume explores the cutting edge of quantitative methods in agricultural risk management and insurance. Composed of insightful articles authored by field experts, focusing on innovation, recent advancements, and the use of technology and data sciences, it bridges the gap between theory and practice through empirical studies, concrete examples and case analyses. Evolving challenges in risk management have called for the development of new, groundbreaking models. Beyond presenting the theoretical foundations of these models, this book discusses their real-world applications, providing tangible insights into how innovative modeling can elevate risk management strategies in the agricultural sector. The latest risk management tools incorporate novel concepts such as index insurance, price index risk management frameworks and risk pools. The practical implications of these approaches are investigated, and their impact on contemporary agricultural risk mitigation and insurance practices is examined. Field experiences illustrate the implementation of these tools and their resulting outcomes. Modern data analysis techniques in agricultural risk and insurance include machine learning, spatial analysis, text analysis, and deep learning. In addition to scrutinizing these ideas, the authors introduce an economic perspective towards risk, highlighting areas that have developed thanks to technological progress. Examples illustrate how these combined methodologies contribute to informed decision-making in agriculture, and their potential benefits and challenges are considered. This carefully compiled volume will be a valuable reference for researchers, practitioners, and students intrigued by the dynamic intersection of agricultural risk management and insurance practices.

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