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.
Pedro José dos Santos Palhinhas Mota
Weekly - 4
Total - 68
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.
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.
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.
Univariate and multivariate models
Non-stationarity and cointegration
Income and volatility
Causality and Forecasting
Propagation of shocks
Poisson Process Statistics
Markov Chain Statistics
Diffusion Models Statistics
Programs where the course is taught: