Vol 8 Issue 2 October 2021-March 2022
Consolata A. Muganda, Sewe Stanley, Winnie Mokeira Onsongo
Abstract: The influence of many climatic variables such as NDVI, minimum and maximum humidity, rainfall, minimum and maximum temperature, and solar radiation make tea production volatile. The clustering of low and high volatility periods values makes determining a suitable GARCH and ARIMA model difficult. Climatic variables are risk measures useful in understanding tea production data. A proper understanding of variables to help monitor and forecast the volatility in tea production output is paramount in applied statistics. The existence of affirmative consent on the standard performance of GARCH(1,1) can be misguiding due to variation in data volatility. The nonlinear nature of tea production and climatic variables creates everlasting interest to scholars to model a forecast of future tea production based on the volatile climatic conditions. We use Box and Jenkins model to outline 63 combinations of ARMA(m,n)-GARCH(p,q) models in tables with m and n are either o, 1, or 2. We use AIC, BIC, and LogL criteria to select the best model. The results based on the rubric indicated that the ARMA(1,1)-GARCH(2,2) is the suitable model.
Keywords: ARMA, Climatic Variables, GARCH, Model fit, Tea Production, Volatility Effect.
Title: Establishing an ARMA-GARCH model fit for volatility effect of Climatic Variables on Tea Production in Tea Zones in Kenya
Author: Consolata A. Muganda, Sewe Stanley, Winnie Mokeira Onsongo
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications