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We use agricultural commodities futures prices to investigate decision making in production when futures prices are governed by jump-diffusion process with seasonal volatility. We derive a preference independent production rule for firms that face both demand and production uncertainty. We compare this rule to the one when only Brownian motion represents the source of risk with constant volatility. Our analysis suggests that for most crops, the jump-diffusion model is sufficiently accurate to guide production decision.

In static framework, many hedging strategies can be settled following the various hedge ratios that have been developed in the literature. However, it is difficult to choose among them the best the appropriate strategy according the to preference or economic behavior of the decision-maker such as prudence and temperance. This is so even with the hedging effectiveness measure. After introducing a hedging ratio that take into account the prudence and temperance of the decision maker, we propose a ranking based approach to measure the effectiveness using L-moment to classify hedge portfolios, hence hedge ratios, with regard to their performance. Moreover, we deal with the hedging issue in presence of quantity and rollover risks and derive an optimal strategy that depends upon the basis and insurance contract. Such hedging issue includes the relevant risks encountered in practice and we relate how insurance contract, specially designed for production risk could affect the futures hedge. The application on futures prices data at hands shows that taking into account quantity and rollover risks leads to better hedging strategy based on the L-performance effectiveness measure.

In agricultural markets, producers incur price and production risks as well as other risks related to production contingencies. These risks impact the producer activity and could decrease his income. The globalization of markets, particularly those of agricultural commodities, provides hedging instruments including futures contracts which will serve to develop a hedging strategy. However, the situation whereby a single futures contract-based positions could offset many risks leads to incomplete market. Especially, an producer looking for better hedging strategy could also include insurance, option contract or mutual funds to further guarantee his income, specially when crop yields are lower than expected.vspace{0.25cm}We investigate the hedging strategies in static framework as well as in continuous time framework. Prior, we analyze the behavior of agricultural prices using various statistical approaches and suggest appropriate price modeling for data at hands. The static hedging strategy also accounts for rollover process which gives raise to additional risks due to spread between new futures and nearby futures and inter-crop hedging. We particularly address hedging strategy that combines futures and insurance contracts. Since decisions making in static framework does not include price changes along the hedging horizon, optimal hedging strategy in continuous time framework will take into account jumps and seasonality by combining futures and option contracts.; Sur les marchés agricoles, les producteurs encourent les risques de prix et de production ainsi que d'autres types de risques liés aux aléas de production. Ces risques impactent l'activité du producteur et pourraient diminuer ses revenus. La mondialisation des marchés, en particulier ceux des matières premières agricoles, permet de développer une stratégie de couverture en utilisant des instruments comme les contrats à terme. Cependant, la situation selon laquelle une position basée seulement sur un contrat futures devrait couvrir tous les risques, entraîne un marché incomplet. Le producteur en recherche de meilleure stratégie de couverture pour ajouter un contrat d'assurance ou d'option pour garantir davantage ses revenus, surtout lorsque les rendements des cultures prévus diminuent. Nous étudions, ici les stratégies de couverture dans le cadre statique, ainsi que dans le cadre de temps continu. Avant, nous analysons le comportement des prix des matières premières agricoles en utilisant diverses approches statistiques afin de suggérer la modélisation des prix adéquate aux données. La stratégie de couverture statique comprend également le processus de retournement de positions qui pourrait entraîner d'autres risques supplémentaires en raison de l'écart entre les nouveaux contrats à terme et des contrats à terme à proximité ainsi que la couverture inter-culture. Nous proposons une stratégie de couverture qui combine des contrats futures et d'assurance. Comme la prise de décisions dans le cadre statique ne tient pas compte des mouvements quotidiens de prix le long de l'horizon de couverture, la stratégie de couverture optimale en temps continu combine des positions en contrat à terme et options tout en prenant en compte les sauts et la saisonnalité dans la dynamique des prix.