Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums
This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with data from futures ma...
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MDPI AG
2025-01-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/13/1/9 |
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| author | Gonzalo Cortazar Hector Ortega José Antonio Pérez |
| author_facet | Gonzalo Cortazar Hector Ortega José Antonio Pérez |
| author_sort | Gonzalo Cortazar |
| collection | DOAJ |
| description | This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with data from futures markets and analysts’ forecasts. Risk premiums are computed by comparing expected and futures prices. The model considers that risk premiums are not solely determined by contract maturity but also by the marketing crop years. These crop years, in turn, are influenced by the respective harvest periods, a crucial factor in the agricultural commodity market. Results show that risk premiums vary across commodities, with some exhibiting positive and others negative values. While maturity affects risk premiums’ size, sign, and shape, the crop year plays a critical role, especially in the case of wheat. As speculators in the financial markets demand a positive risk premium, its sign provides insights into whether they are buyers or sellers of futures for each crop year, maturity, and commodity. This research offers valuable insights into grain price behavior, highlighting their similarities and differences. These findings have significant practical implications for market participants seeking to refine their trading and risk management strategies and for future research on the industry structure for each crop. Moreover, this enhanced understanding of risk premiums can be directly applied in the finance and agricultural industries, improving decision-making processes. |
| format | Article |
| id | doaj-art-f54f2df17415477d8ed0b80e5eb61975 |
| institution | Kabale University |
| issn | 2227-9091 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Risks |
| spelling | doaj-art-f54f2df17415477d8ed0b80e5eb619752025-01-24T13:48:19ZengMDPI AGRisks2227-90912025-01-01131910.3390/risks13010009Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk PremiumsGonzalo Cortazar0Hector Ortega1José Antonio Pérez2Ingeniería Industrial y de Sistemas, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago 8331150, ChileIngeniería Industrial y de Sistemas, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago 8331150, ChileIngeniería Industrial y de Sistemas, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago 8331150, ChileThis paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with data from futures markets and analysts’ forecasts. Risk premiums are computed by comparing expected and futures prices. The model considers that risk premiums are not solely determined by contract maturity but also by the marketing crop years. These crop years, in turn, are influenced by the respective harvest periods, a crucial factor in the agricultural commodity market. Results show that risk premiums vary across commodities, with some exhibiting positive and others negative values. While maturity affects risk premiums’ size, sign, and shape, the crop year plays a critical role, especially in the case of wheat. As speculators in the financial markets demand a positive risk premium, its sign provides insights into whether they are buyers or sellers of futures for each crop year, maturity, and commodity. This research offers valuable insights into grain price behavior, highlighting their similarities and differences. These findings have significant practical implications for market participants seeking to refine their trading and risk management strategies and for future research on the industry structure for each crop. Moreover, this enhanced understanding of risk premiums can be directly applied in the finance and agricultural industries, improving decision-making processes.https://www.mdpi.com/2227-9091/13/1/9futuresrisk premiumanalysts’ forecastscommodities |
| spellingShingle | Gonzalo Cortazar Hector Ortega José Antonio Pérez Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums Risks futures risk premium analysts’ forecasts commodities |
| title | Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums |
| title_full | Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums |
| title_fullStr | Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums |
| title_full_unstemmed | Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums |
| title_short | Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums |
| title_sort | using futures prices and analysts forecasts to estimate agricultural commodity risk premiums |
| topic | futures risk premium analysts’ forecasts commodities |
| url | https://www.mdpi.com/2227-9091/13/1/9 |
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