Risk Exposition of Prices in Agricultural Commodities Using Options and Futures

Changes and fluctuations in commodity prices exert different effects on value chain participants, depending on the position they have in the chain. Agricultural commodities are exposed to a set of different factors influencing the prices of the commodities. They are influenced by the season, weather shocks, demand and supply forces, household income, tastes and preferences of the consumers. Observing the most recent history, high price fluctuations have been observed during the financial crisis in 2008. One out of many approaches for hedging the price risk is the usage of financial derivatives. This study will be concerned with the volatility modelling methods with the help of futures and options for corn and soya. Methods used for modelling the volatility a Black Scholes Implied Volatility. The simplest method in ARCH family, namely the GARCH (1,1) method will be used for modelling volatility based on the historical futures data dating back to 2005. The implied volatility is derived solving back the Black – Scholes Model, only this time looking for sigma. The sole purpose of the thesis is to examine which of the two methods has a better predictive power. Model comparison is done with the help of forecast regression models. The regression models have shown the difficulty in assessing which model has more accurate predictive power. The Adjusted R2 for both models in both cases is relatively low. However, the GARCH (1,1) model has slightly higher values for this indicator. Even the GARCH (1,1) model h a better performance, due to the relatively low adjusted R2 values, no stable conclusion regarding the model performance can be derived. assets(Hull, 2008). Options are financial instruments in the form of a contract that are traded between the writer of an option and an option holder, and it provides the re GARCH and