Markov Chain Monte Carlo Approach to the Analysis and Forecast of Grain Prices and Volatility Monitoring
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Fecha
2022-11
Autores
Lozano-Orozco, Gabriela
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ITESO
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Descripción
Public studies on the dynamics of food staples as important as cereals (grains) are relatively scarce. Here we undertake a preliminary analysis of the time series for corn, wheat, soybean, and oat prices first via classical ARIMA/GARCH models, and later complementing with the more complex Stochastic Volatility (SV) models. The goal is to improve upon the classical results by implementing a Bayesian analysis through the construction of a suitable Markov Chain Monte Carlo Model with improved volatility analysis and forecasting capabilities. The performance of the SV model is benchmarked against the classical ARMA/GARCH approach, and both are discussed as monitoring tools for the volatility prices.
Palabras clave
Mcmc, Markov Chain Monte Carlo, Grains, Stochastic Volatility Models
Citación
Lozano-Orozco, G. (2022). Markov Chain Monte Carlo Approach to the Analysis and Forecast of Grain Prices and Volatility Monitoring. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESO.