2.0

credits

Average Course Rating

This course covers both theoretical and practical aspects of modern econometric models that are used by financial institutions, investment banks, central banks, governments, and other research institutes. The students are introduced to models in Finance that feature nonlinearities, time-varying parameters and latent variables. The objective of this course is to teach students how to design, code, estimate and analyze these models; understand the interplay between econometric techniques and modeling assumptions; gain experience in working with real data. The course includes a selection of the following topics: time-series modelling (analysis of vector autoregressive models, time series models with regime switches and time-varying coefficients, as well as dynamic factor models); hypothesis testing; omitted variables and misspecification; measurement error and instrumental variables; predictability of asset returns; event study analysis; econometric tests of the CAPM and multifactor models; volatility modelling, tools and applications of dynamic predictive modeling in econometric “Big-Data” environments. For the most part, we will focus on Bayesian methods of inference, with detailed discussions of suitable Markov-Chain-Monte-Carlo methods.