Quantitative Risk Management in Agricultural Business (Hardcover)

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.

Hirbod Assa is a prominent researcher in the field of InsurTech and risk management with a focus on parametric risk transfer tools. Hirbod serves as a director at Model Library, a consulting firm specializing in risk management. Additionally he was a founding team and quantitative researcher at Edge Technologies, working on innovative risk management solutions. His academic career includes ten years at the universities of Essex, Kent, and Liverpool, focusing on commodity and systematic risk management by leveraging insurance risk capacity. Hirbod played a key role in the development of price index insurance for agricultural commodities at Stable Group Ltd, an achievement recognized by the University of Liverpool in their 2021 REF impact case submissions. He worked on machine learning projects with renowned banks such as Lloyds and MUFG. Hirbod holds a Ph.D. in financial mathematics from the University of Montreal and a Ph.D. in economics from Concordia University.

Peng Liu is a Lecturer in the School of Mathematics, Statistics and Actuarial Science, University of Essex since 2020. He received PhD in Probability and Statistics in Nankai University in 2015. Since then, he did postdoc in the University of Lausanne and University of Waterloo for two years respectively. His research focuses on Quantitative Risk Management, Actuarial Science,  and Extreme Value Theory.

Simon (Meng) Wang is the Chief Technology Officer at Stable Group Limited, a London-based leading InsurTech firm specializing in supply chain parametric insurance. With a strong foundation in mathematical sciences and financial mathematics, Simon has extensive experience in developing advanced machine learning algorithms and quantitative models for risk management and financial derivatives. His innovative work includes the creation of automated systems for real-time hedging in incomplete markets and comprehensive risk simulation tools. Simon has also contributed to several notable publications in the field, focusing on cross-hedging, stochastic models, and agricultural goods pricing.