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Econometric and Financial Statistics

Objectives

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

Code

10813

Credits

6.0

Responsible teacher

Pedro José dos Santos Palhinhas Mota

Hours

Weekly - 4

Total - 68

Teaching language

Português

Prerequisites

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

Bibliography

  • 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 carried out through the accomplishment of individual works delivered on the form of written report, with presentation and discussion in class or by means of written tests.

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

Programs where the course is taught: