Quantitative Risk Management in Agricultural Business

The collection of chapters presented here highlights the latest advancements in insurance, focusing on the integration of cutting-edge AI and statistical techniques with innovative concepts such as parametric and price insurances. These studies address a wide range of topics, from theoretical developments to practical applications and empirical results, pushing the boundaries of traditional insurance models. This introduction summarizes the key contributions of each paper, emphasizing their innovative approaches and their relevance to contemporary insurance practices.

Index insurance has become a notable risk management tool in response to increasing climate variability and extreme weather events. This chapter offers a thorough review of innovative index-based financial solutions, focusing on index insurance. It explores the essential principles of index insurance, including its actuarial framework, empirical research findings, and practical considerations. Additionally, the chapter explores future advancements in the field, emphasizing the integration of cutting-edge technologies such as artificial intelligence and blockchain. These innovations have the potential to risk modeling, underwriting and claims processing of index insurance. Aimed at researchers, practitioners, and policymakers, this chapter serves as a comprehensive guide for designing effective index insurance programs that enhance resilience in the face of climate uncertainties.

Agriculture is the most vulnerable sector to climate change, e.g., temperatures or rainfall may significantly affect the crop yields, also leading the proliferation of pathogens and hence pests and diseases [1]. The total economic losses from weather- and climate-related have caused damages reaching nearly 487 billion of euros in EEA member countries since 1980, and just 3% of all events are responsible for 60% of economic losses [2]. Extreme weather events such as heavy precipitation, flood, drought, frost, heat, and strong wind are more and more frequent, intense, long-lasting, and they are the major drivers of agricultural losses [3, 4]. Heavy precipitation may reduce photosynthetically active radiation up to irreversible tissue damages, setting the conditions for diseases due to the proliferation of pathogens, nutrient leaching, soil erosion, and oxygen deficit [5, 6], also inducing flash flood events, in combination with other factors as the antecedent soil moisture [7, 8]. Drought and water shortage may affect the metabolism of plants with changes in root growth and architecture, and other tissue-specific responses that modify the flux of cellular signals [9]. The stress due to drought events is the main factor limiting the development of crop and its productivity [10]. Cold may damage the leaf and seedling survival, also leading to the sterility and the abortion of formed grains, especially for the cereal crops [11]. Heat directly affects the crop physiology, reducing photosynthesis rates, leading the acceleration of leaf senescence processes, oxidative damages, and pollen sterility [12]. Strong wind may also be very impactful (i.e., abrasions on the leaves and fruits, defoliations, water loss, desiccation, loss of flowers and poor fruit set), although the plants can change the structure and properties of cells and tissues, re-configuring their canopies as a defensive response [13]. On-farm and risk-sharing strategies are available to improve the resilience of farming systems to weather risks. The former includes risk control (i.e., risk prevention such as irrigation, shading, pest control, improved planning and monitoring activities), reserves (i.e., stocking, financial savings, additional labour input), and diversification (i.e., agricultural and structural diversification as nature conservation or agrotourism, off-farm allocation of resources); the latter includes risk pooling (i.e., mutual funds, agricultural insurance, membership in cooperatives, credit unions, producer organizations), and risk transfer (i.e., forwards, futures contracts) [14]. Member States may grant support for risk management tools (e.g., financial contribution to insurance premiums and to mutual funds) which can help farmers to manage production and income risks related to their agricultural activity and over which they have no control [15]. The new Common Agricultural Policy (CAP) reform is putting increasing emphasis on instrument supporting proactive management of the effects of extreme weather events due to climate change [15]. We provide an overview of the spread of risk management tools subsidised by new CAP 2023–2027, focusing on two promising tools: the weather index-based insurance and the Agri-CAT fund. We also discuss on their feasibility at farm-level, highlighting pros and cons, also animating the debate on how policymakers may improve the attractiveness of risk management tools.

This chapter focuses on assessing avocado production index insurances and investigates insurance pricing utilizing credibility theory on a heterogeneous data set. The paper presents a methodology for analyzing and designing insurances, specifically addressing the challenges that arise when dealing with a data sets with varying characteristics. To enhance the reliability of the results, the analysis modifies Bühlmann’s credibility theory to refine parameter distributions. Considering data sets from different countries on avocado production, this chapter provides global and local premium rates for each country, revealing rates based on historical trends. The study also proposes a two-layer policy for insurances that covers production return risks, incorporating both a standard deduction and a preventive measure to mitigate moral hazard risks.

This paper focuses on designing and pricing commodity derivatives and insurance within a novel financial engineering framework that can be subsequently tested empirically using commodity price data. Optimal contract solutions are obtained and interpreted. We quantify explicitly how derivative prices and insurance premiums are affected by economic variables linked to commodity supply and demand. Our results generalize some existing commodity derivative pricing models and further show under which conditions there will be no trading of derivative instruments and insurance. We report GMM estimates of the model parameters for a large dataset of commodity futures. These results also contribute to a better understanding of the “financialization” of commodities.

This paper examines different hedging techniques for options written on non-exchange-traded agricultural commodities using the futures markets to hedge, and evaluates the performance using statistical measures. The paper applies the hedging methods to real agricultural commodity data from the USDA. In these markets there are a number of commercial risks, such as weather and supply-chain disruption, which need to be managed by both producers and consumers. Typically, there is no perfectly correlated hedging instrument available for the product being traded, and as such there is basis risk present when trying to find a hedging solution. This highlights the need for empirical studies which address the problem of how to hedge in this environment. We evaluate static and dynamic hedging strategies for European options written on livestock indices using live cattle futures to hedge. Hedging methods based on delta, minimum variance, value-at-risk (VaR), and conditional VaR (C-VaR) are tested. Hedging performance is examined by hedging effectiveness (i.e calculating risk reduction versus an unhedged portfolio) and distribution statistics. Overall, we found that the static minimum-variance technique provided the best hedging performance in terms of risk reduction versus the unhedged portfolio.

Farming is among the most vulnerable segments of society due to the source of the income that is highly dependent on environmental risks. To maintain their production, farmers, who are critical components of agricultural production, need to protect themselves against production risks. For farmers to continue agriculture, it is crucial to provide insurance policies that at the very least protect their current income. Therefore, crop yield insurance has been discussed in this study. When a crop yield falls short of a predetermined threshold, crop yield insurance compensates for the resulting yield loss. This insurance product holds a prominent position among other agricultural insurances because yield insurance, which aims to keep agricultural production at a specific level, maintains sustainability in the ecosystem. Through the spatiotemporal modeling of crop yields and yield insurance, the impact of climate change, a major problem for agricultural insurance, has also been addressed. For the conditional crop yield distribution in this study, a hierarchical Bayesian technique is employed to characterize the spatiotemporal dependence. Wheat yield statistics from the years 2004 to 2022 were used for a total of 47 districts that are part of Ankara and Konya, which are at the top of the list in terms of wheat production volume. Premium rates have been obtained for the region, province, and chosen districts using the preferred model in accordance with model selection and performance criteria, and the results are presented. The R-INLA package program is used to perform all statistical analyses for this study.

Multiple-peril crop insurance policies require statistical modeling of probability distributions of crop yields. Unfortunately, no single parametric distribution is likely to capture the true data generating process. Likewise, non-parametric approaches converge to the true distribution at a slow rate; yield histories are often of modest size. Recognizing these shortcomings, model and forecast combination are now being applied in crop insurance settings. Model and forecast combination avoid the dangers inherent in selecting a single model for the predictive yield distribution. The component models for the combination can be selected ad-hoc or based on the idea of distributional similarity. Crop yields are spatially correlated, so the model for one insured unit may be related to another, and can then be used in the pool of potential models for the combination.

We briefly review the literature on model and forecast combination and its application in agricultural insurance settings. We then turn toward an empirical application involving crop yield insurance for major row crops in the U.S. Southeast. A variety of individual models and combinations are estimated at the county level. Insurance premiums and premium rates are calculated from the estimated distributions. Implications of model and forecast combination for insurance rates, premiums, and government subsidies are discussed. We conclude by suggesting future research in this area.

In this paper, we apply a recursive method to financial data to determine their corresponding Hurst exponent and the optimal Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. We begin by introducing the long-range dependence phenomenon and methods to address it in time series modeling. Then, a recursive algorithm, where the Hurst exponent is estimated by applying an autoregressive filter to the data repeatedly until it converges, is empirically tested with simulated data for stability and convergence. Finally, we apply this convergence approach to real commodity data sets. We identify the optimal ARFIMA models for each commodity studied and estimate the Hurst exponent as well as their corresponding ARFIMA parameters. Our results provide a stable method for estimating the Hurst index and fitting stationary long-memory processes to ARFIMA models.

In this chapter, we explore the application of machine learning (ML) and deep learning (DL) techniques to forecast commodity price volatility, emphasizing the integration of climatic data and financial variables. We use an XAI method, namely the Shapley interpretation method, to explain the impact of different variables on the agricultural price risk. As a preliminary consideration, agricultural businesses are supposed to be significantly influenced by environmental factors, particularly climatic anomalies such as El Niño and La Niña. Therefore, understanding their impact is crucial for effective market prediction and risk management. We discuss various predictive models, including time series analysis, machine learning models, and recurrent neural networks (RNNs) , highlighting their ability to handle large datasets and complex patterns. This chapter provides a comprehensive overview of how advanced computational methods can enhance the accuracy of volatility forecasts, to show the substantial benefits for farmers, investors, and policymakers. By integrating diverse data sources, including historical price data and environmental indicators, while illustrating the potential of ML and DL to study commodity trading and financial planning, we observe that climate features do not persistently rank among the top predictors of agricultural price risk in the US market. This might look surprising at first, as the common belief is the great influence of climate on any aspect of agriculture. This can be interpreted as a sign of adequately manageable risk in commodity market prices against natural phenomena.

This chapter is concerned with textual and sentiment analysis in agriculture commodities market using the natural language processing (NLP) methods. There are extensive research on textual and sentiment analysis in financial markets however, most of them are focusing on equity market and a minority on other commodities like energy commodities. Therefore, this chapter first reviews research works on textual and sentiment analysis in agriculture market in general. Then, presents textual analysis methods that can be carried out to study the effect of textual data and sentiment in agriculture market. Finally, it presents an example of implementing a topic modelling task and textual regression for forecasting realized volatility of corn returns. To the best of the author’s knowledge, there is no study focusing on textual regression in agriculture market. Additionally, the studies conducting textual sentiment analysis are very limited. In this spirit, this study tries to fill this gap by introducing both well established and new textual and sentiment analysis methods to the agricultural researchers community. The limited experiment carried out with these methods in the present research testifies the superiority of the text-based models in explaining future movements of corn’s volatility. More specifically, the results of one-month-ahead realized volatility regression indicates statistically significant superior performance of both direct textual regression and sentiment regression compared to traditional methods like HAR and ARIMA. In addition, as the most accurate method, textual regression’s accuracy stands higher above that of the sentiment regression model.

In this chapter, we delve into the application of Singular Spectrum Analysis (SSA) for the examination and prediction of agricultural financial time series data. The erratic nature of agricultural markets is shaped by various factors, including seasonal trends, climatic conditions, and economic directives, posing a significant challenge for analysis. SSA stands out with its capacity to break down a time series into discernible components like trend, oscillatory elements, and noise, providing a sophisticated lens to interpret market dynamics.

The study utilizes SSA on a diverse array of agricultural financial time series data, including Fruit Planted Area, Fruit Home Production, Boxed Beef Prices for Choice and Select cuts, and CO2 Emission Intensity for rice commodities in European countries. We aim to achieve two primary goals: first, to unearth the intrinsic patterns and tendencies that dictate the movements of agricultural financial time series; and second, to project future trends, concentrating on enhancing strategies for investment and policymaking. Our findings highlight the prowess of SSA in sifting through the noise to uncover periodic behaviors and anomalies that conventional analysis might miss. The predictive model, founded on the reassembled components, exhibits notable precision in forecasting imminent price fluctuations, offering crucial insights to participants in the agricultural finance arena.

This research not only reaffirms the value of SSA in the realm of financial time series analysis but also sets the stage for its broader adoption in sectors where decoding intricate, non-linear patterns is of essence.