Application of Hybrid seasonal ARIMA-GARCH Model in modelling and forecasting fertilizer prices in Kenya
Date
2023
Authors
Okello, E. A.
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Publisher
Strathmore University
Abstract
Volatility in fertilizer prices pose a huge risk to both farmers and suppliers. To manage fertilizer price volatility, a more efficient price risk management model is necessary. Stand alone models have been criticized for failing to capture the true market conditions by capturing only the unilateral information. Better outcomes have been credited to combined models, such time series models. Existing models have factored in variables such as natural gas, transport, volumes traded, crude oil prices, corn prices, ethanol, market concentration and regions. In this study, the port through which fertilizer is imported is taken into account while creating a Hybrid SARIMA-GARCH model, which is then used to anticipate pricing. Using RMSE, MAE, and MASE, the model’s predictive abilities were assessed. The findings of this study suggest that the best model for the port of Gulf is SARIMA models (1, 1, 0) (2, 1, 0)12, with an AIC = 997.53, and RMSE = 5.6015, and can efficiently capture the pricing behaviour in this port. In Yuzhny, Hybrid SARIMA (2, 1, 0) (2, 1, 0)12–GARCH (1, 1) turned out to be the best fit with AIC = 7.4389, RMSE = 7.5802, MAE=5.4797 and MASE=0.6885. The study concludes that the port through which fertilizer is imported has an effect on the price placed as each of the ports under study yielded a unique model.
KEY WORDS: Nonlinear time series, Heteroscedasticity, SARIMA model, GARCH model, Hybrid SARIMA GARCH model, Ljung–Box test, Augmented Dickey Fuller test.
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Citation
Okello, E. A. (2023). Application of Hybrid seasonal ARIMA-GARCH Model in modelling and forecasting fertilizer prices in Kenya [Strathmore University]. http://hdl.handle.net/11071/15390