Excessive food price variability early warning system: Incorporating exogenous covariates
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 mark...
| Autores principales: | , , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
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International Food Policy Research Institute
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/143600 |
| _version_ | 1855540888303828992 |
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| author | Geng, Xin Hernandez, Manuel A. Martins-Filho, Carlos |
| author_browse | Geng, Xin Hernandez, Manuel A. Martins-Filho, Carlos |
| author_facet | Geng, Xin Hernandez, Manuel A. Martins-Filho, Carlos |
| author_sort | Geng, Xin |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Artículo preliminar |
| id | CGSpace143600 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1436002025-12-08T10:11:39Z Excessive food price variability early warning system: Incorporating exogenous covariates Geng, Xin Hernandez, Manuel A. Martins-Filho, Carlos models maize soybeans food security price volatility food prices wheat 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. 2021-09-27 2024-05-22T12:15:30Z 2024-05-22T12:15:30Z Working Paper https://hdl.handle.net/10568/143600 en https://hdl.handle.net/10568/154182 Open Access application/pdf International Food Policy Research Institute Geng, Xin; Hernandez, Manuel A.; and Martins-Filho, Carlos. 2021. Excessive food price variability early warning system: Incorporating exogenous covariates. IFPRI Technical September 2021. Washington, DC: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/p15738coll2.134592>. |
| spellingShingle | models maize soybeans food security price volatility food prices wheat Geng, Xin Hernandez, Manuel A. Martins-Filho, Carlos Excessive food price variability early warning system: Incorporating exogenous covariates |
| title | Excessive food price variability early warning system: Incorporating exogenous covariates |
| title_full | Excessive food price variability early warning system: Incorporating exogenous covariates |
| title_fullStr | Excessive food price variability early warning system: Incorporating exogenous covariates |
| title_full_unstemmed | Excessive food price variability early warning system: Incorporating exogenous covariates |
| title_short | Excessive food price variability early warning system: Incorporating exogenous covariates |
| title_sort | excessive food price variability early warning system incorporating exogenous covariates |
| topic | models maize soybeans food security price volatility food prices wheat |
| url | https://hdl.handle.net/10568/143600 |
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