Finance and insurance

Introduction to R and RStudio. Review of basic and inferential statistics in R; graphical representations in R. Simple and multiple linear regression model (review). Classical approach to time series analysis in R; decomposition and smoothing techniques in R. Modern approach to time series analysis: specification, estimation, and diagnostic checking of ARIMA models in R. Specification, estimation, and diagnostic checking of GARCH models in R. Analysis and forecasting with applications to macroeconomic and financial data in R.

Basic knowledge of statistics

Instructor’s lecture notes Supplementary readings: Cryer J.D., Chan K.S. (2008) Time Series Analysis With Applications in R (2a ed) Venables, W.N., Smith D.M., and R Development Core Team (2025) An introduction to R ( https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf )

Not mandatory, but strongly recommended.

Written test with the aid of R software for real time serie analysis (1 hour). The student must demonstrate be able to perform a statistical analysis in the R environment and provide a proper interpretation of the results.

Traditional (face-to-face) teaching with laboratory experiences.