Díaz-Ruelas, Álvaro P.Lozano-Orozco, Gabriela2023-01-262023-01-262022-11Lozano-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.https://hdl.handle.net/11117/8437Public 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.enghttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfMcmcMarkov Chain Monte CarloGrainsStochastic Volatility Modelsinfo:eu-repo/semantics/masterThesis