Excessive food price variability early warning system: Incorporating exogenous covariates - Geng, Xin; Hernandez, Manuel A.; Martins-Filho, Carlos (International Food Policy Research Institute, 2021)
The Excessive Food Price Variability Early Warning System, maintained by IFPRI’s Food Security Portal, identifies periods of unusual or excessive price volatility (i.e. price variability that exceeds a pre-established, estimated threshold) for a wide set of major agricultural and food commodity markets. The Tool is based on nonparametric estimators for conditional value-at-risk (CVaR) and conditional expected shortfall (CES) associated with conditional distributions of the modeled price returns series, developed by Martins-Filho et al. (2015, 2018). The estimations are performed on a day-to-day basis and intend to provide timely price variability alerts to makers, traders, and farmers worldwide. This incorporates exogenous covariates into the Tool when modeling the stochastic behavior of commodity price returns. We focus on maize, wheat, and soybeans, which are key agricultural commodities, and add three exogenous variables when estimating the conditional mean and variance of their daily price returns. The exogeneous covariates include oil price returns, exchange rate percentage variations (US dollar depreciation rate), and Standard & Poor index (S&P500) returns, which are available on a daily frequency and are widely used when modeling dynamic price relationships in agricultural commodity markets (see, e.g., Deb et al., 1996; Ai et al., 2006; Gilbert, 2010; Natanelov, 2011; Gardebroek et al., 2016). The objective of the is twofold. First, we assess whether the behavior of agricultural commodity prices can be associated with energy (input), macroeconomic, or financial factors by depicting the relationships between variations in specific exogenous covariates and agricultural price returns and volatility. Second, we evaluate whether incorporating these covariates can help improve the predictive performance of the Tool in terms of more accurately identifying periods of excessive volatility in agricultural markets, relative to a model without exogenous variables that only includes lagged own agricultural price returns as covariates. The period of analysis is January 1990 through December 2020.