FUTURE PRICE INDEX OF FARM PRODUCTS BASED ON CLIMATE FACTORS

Date

2024-7-3

Author

KARASU, NURŞEN

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Climate conditions have a big impact on the yield of farm products, hence the prices. This thesis makes price prediction of majorly traded grains, Wheat, Barley and Corn, based on major climate conditions, total precipitation and dew point, levels of which are taken from European Centre for Medium-Range Weather Forecast (ECMWF) database of Konya, Polatli, Yozgat, Adana and Urfa where major production is held, by using a smart, machine learning method of ensemble training and compares with the price prediction results retrieved by simple regression method. These predictions would help future prices in futures commodity markets to be determined and any desired future price index can then be retrieved through these settled end of day future prices, by applying weights decided by index structurers. The study also confirms that ensemble training method could be used for future price prediction even when the statistic significance of data is low, therefore a successful simple regression optimization methodology is hard to apply. On the other hand, high volatile inflation rate and exchange rate would lead the predictions to deviate outside the accepted limits so the model studied in this paper is believed to be a better fit in stabilized economies. This thesis study offers important clues for producers for their production preferences, policy makers to build production planning, insurance companies to make climate risk mappings and financial insrument traders to make reasonable pricing so to prevent volatility in commodity market. Finally, all thesis study outputs are expected to serve the purpose of maintaining a sustainable agricultural production.

Subject Keywords

Climate Index, Machine Learning Models, Ensemble Training Methods, Future Price Index on Farm Products, Sustainability, Agricultural Risk Index

URI

https://hdl.handle.net/11511/110139

Collections

Graduate School of Applied Mathematics, Thesis