Dates price risk management using futures markets tools (Bivariate GARCH model)

Agricultural activity is full of risks. In this activity, various natural, social and economic risks have created a fragile and vulnerable situation for producers. Price risk in agricultural products has caused financial problems for many producers and farmers. To deal with these price risks and price fluctuations, there are a variety of tools. This study focused on futures markets instruments as risk management tools for date's price risks. This study uses monthly futures prices of dates and using mean-variance framework, the optimal hedge ratio with OLS method would determined. Due to The conditional variances autocorrelated in residuals in regression models time varying hedge ratios with Bivariate GARCH models is determined. Bivariate GARCH model is used in this study to determine hedge ratios. Therefore, first the time varying conditional variance covariance matrix with using multivariate models based on heterogeneous variance BEKK (1, 1) is estimated. Then, using the results of this matrix, the optimal time varing hedge ratios were calculated. Dates future price is predicted using artificial neural networks pattern and the GARCH model. The results of hedge performance show that time varying hedge ratios are eliminated more price risk than the OLS method. The results show that the averaged hedge ratios of Bivariate GARCH model is 0.7, meaning that about 70% of the date's price risk, could be reduced with sales in the futures market.