Maximum likelihood estimation in multivariate lognormal diffusion process with a vector of exogenous factors

• Autores: Ramón Gutiérrez Jáimez, Ramón Gutiérrez Sánchez, Ahmed Nafidi
• Localización: VIII Journées Zaragoza-Pau de Mathématiques Appliquées et de Statistiques / coord. por Manuel Pedro Palacios Latasa, David Trujillo, Juan José Torrens Iñigo, Monique Madaune-Tort, María Cruz López de Silanes Busto, Gerardo Sanz Sáiz, 2003, ISBN 84-7733-720-9, págs. 337-346
• Idioma: inglés
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• Resumen

  • In this paper we consider a new model of multivariate lognormal diffusion process with a vector of exogenous factors such that each component exclusively affects the respective endogenous variable of the process. Starting from the Kolmogorov differential equations and Ito's stochastics equation of this model, its transition probability density is obtained. A discrete sampling of the process is assumed and the associated conditioned likelihood is calculated. By using matrix differential calculus, the maximum likelihood matrix estimators are obtained and expressed in a computationally feasible form. This model, an extension of previously studied lognormal diffusion processes ([1],[2],[3]), extends the possibility of applications of lognormal dynamic modelling in Economics, Population Growth, Volatility,etc.