Econometric and Financial Statistics


This curricular unit provides mathematical and econometric statistics tools that allow the treatment of financial models, considering continuous time or discrete time, in order to obtain results regarding the relationship between different variables, in particular income and volatility, and as to causality and prediction. Also analyzed are possible regime changes, such as "stability" versus "crisis" situation or the switch between bull and bear.

General characterization





Responsible teacher

Pedro José dos Santos Palhinhas Mota


Weekly - 4

Total - 68

Teaching language



Knowledge of Probability and Statistics, Stochastic Processes,  Mathematical Analysis and Algebra.


  • Brockwell, P.J. and Davis, R.A., Time Series: theory and methods. Springer, 1991.
  • Brooks, C., Introductory Econometrics for Finance, Cambridge University Press, 2008.
  • Campbell, J., Lo, A. and MacKinlay, A., The Econometrics of Financial Markets, Princeton University, 1997.
  • Durret, R., Essential of Stochastic Processes. Springer, 2012.
  • Prakasa Rao, B.L.S., Statistical Inference for Diffusion Type Processes. Arnold, London and Oxford University press, New York, 1999.

Teaching method

Classes work in a practical theoretical regime.

In the classes the theoretical concepts are exposed, some demonstrations are carried out simultaneously illustrating their application through examples and exercises.

A substantial part of the study is done in the student''s autonomy, with the aid of notes and other bibliographical supports, and with the support of teachers to clarify doubts at pre-established times.

Evaluation method

The evaluation is of seminar type and is carried out through the accomplishment of individual projects delivered on the form of written reports. Both individual projects are valued between 0 and 20 and the final grade is the arithmetic mean of the individual projects classification.

The same evaluation method is applied to both continuous and suplementary evaluation.

Subject matter

  1. Statistical-Econometric fundamentals
  2. Univariate and multivariate models
  3. Non-stationarity and cointegration
  4. Income and volatility
  5. Causality and Forecasting
  6. Propagation of shocks
  7. Factors Models
  8. Poisson Process Statistics
  9. Markov Chain Statistics
  10. Diffusion Models Statistics


Programs where the course is taught: