Advances in statistical inference for econometric diffusion models

• Autores: Alejandra María López Pérez
• Directores de la Tesis: Wenceslao González Manteiga (dir. tes.), Manuel Febrero Bande (dir. tes.)
• Lectura: En la Universidade de Santiago de Compostela ( España ) en 2022
• Idioma: español
• Tribunal Calificador de la Tesis: Eva Ferreira García (presid.), Juan Carlos Reboredo Nogueira (secret.), Nuno Miguel Baptista Brites (voc.)
• Programa de doctorado: Programa de Doctorado en Estadística e Investigación Operativa por la Universidad de A Coruña; la Universidad de Santiago de Compostela y la Universidad de Vigo
• Materias:
  • Matemáticas
    • Estadística
      • Calculo en estadística
      • Teoría estocástica y análisis de series temporales
      • Técnicas de inferencia estadística
• Enlaces
  • Tesis en acceso abierto en: MINERVA
• Resumen

  • Due to their analytical tractability, continuous-time models have become a centerpiece in the financial literature. The goal of this thesis is the development of new goodness-of-fit test for continuous-time diffusion models, considering stochastic differential equations with deterministic and stochastic volatility and Itô diffusions as functional time series. Notwithstanding the importance of goodness-of-fit tools, latent factors and a continuous-time setting with observations occurring at discrete time points challenge the estimation of the models. Therefore, the estimation problem is addressed, as it hinders the goodness-of-fit procedures, discussing the intricacies of different estimation implementations prior to the methodological contribution of the test procedures.