An analysis of East African tea crop prices using the MCMC approach to estimate volatility and forecast the in-sample value-at-risk

Abstract

In Eastern African countries, agriculture contributes significantly to the national economy. However, the prices of the essential crops fluctuate considerably due to climate change, the economic crisis, and surging food and fuel costs in the region. This paper aims to capture the dynamic volatility of tea crop prices, one of the region's most important cash crops. We applied the Markov-switching GARCH (MS-GARCH)-type specifications with different scedastic functions and error distributions to estimate volatility and forecast the in-sample value-at-risk (VaR) of tea price returns. This paper analyses monthly tea auction prices (i.e., Mombasa auction) in USD from January 1980 to June 2022. The parameters of the MS-GARCH model are estimated in a Bayesian framework via the MCMC approach. The findings evidenced that the EGARCH skewed Student-t model with three regimes was superior in estimating volatility. In terms of VaR evaluation, there is no superior method, but considering scedastic functions, fat tails, asymmetry, and regime switching is more effective. To manage volatility and risk in the East African tea sector, investors should consider regime switching GARCH models.