Markov Chain Monte Carlo Approach to the Analysis and Forecast of Grain Prices and Volatility Monitoring

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dc.contributor.advisorDíaz-Ruelas, Álvaro P.
dc.contributor.authorLozano-Orozco, Gabriela
dc.date.accessioned2023-01-26T20:16:32Z
dc.date.available2023-01-26T20:16:32Z
dc.date.issued2022-11
dc.descriptionPublic 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.es_MX
dc.identifier.citationLozano-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.es_MX
dc.identifier.urihttps://hdl.handle.net/11117/8437
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectMcmces_MX
dc.subjectMarkov Chain Monte Carloes_MX
dc.subjectGrainses_MX
dc.subjectStochastic Volatility Modelses_MX
dc.titleMarkov Chain Monte Carlo Approach to the Analysis and Forecast of Grain Prices and Volatility Monitoringes_MX
dc.typeinfo:eu-repo/semantics/masterThesises_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX

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