Modeling volatility and dependency of agricultural price and production indices of Thailand: Static versus time-varying copulas

Abstract

Volatility and dependence structure are two main sources of uncertainty in many economic issues, such as exchange rates, future prices and agricultural product prices etc. who fully embody uncertainty among relationship and variation. This paper aims at estimating the dependency between the percentage changes of the agricultural price and agricultural production indices of Thailand and also their conditional volatilities using copula-based GARCH models. The motivation of this paper is twofold. First, the strategic department of agriculture of Thailand would like to have reliable empirical models for the dependency and volatilities for use in policy strategy. Second, this paper provides less restrictive models for dependency and the conditional volatility GARCH. The copula-based multivariate analysis used in this paper nested the traditional multivariate as a special case (Tae-Hwy and Xiangdong, 2009) [13]. Appropriate marginal distributions for both, the percentage changes of the agricultural price and agricultural production indices were selected for their estimation. Static as well as time varying copulas were estimated. The empirical results were found that the suitable margins were skew t distribution and the time varying copula i.e., the time varying rotate Joe copula (270°) was the choice for the policy makers to follow. The one-period ahead forecasted-growth rate of agricultural price index conditional on growth rate of agricultural production index was also provided as an example of forecasting it using the resulted margins and time-varying copula based GARCH model.