Financial Time Series

Recommended Prerequisites

Time Series Analysis, Stochastic Processes and Calculus, Financial Economics.

Teaching Methods

Theoretical-Practical classes where the models and estimation techniques are presented. It is shown how to use these to study financial series with recourse to a proper software. Exercises solved in the classroom.

Learning Outcomes

It is aimed at analyzing the stylized facts usually observed on financial time series, by using (and if necessary generalizing) the knowledge acquired in Time Series. The purpose is that the students be aware of the theoretical foundations of the models used to analyze these series and of the set of techniques used, namely with recourse to adequate software, for estimation and forecasting.

Work Placement(s)

No

Syllabus

1. Notions and results on Statistics, Stochastic Processes and Financial Economics.

2. Description of the stylized facts observed on financial series (clustering and persistence, in special in volatility, large tails in the return distributions, extreme observations, …).

3. Linear Regression.

4. Analysis of Non stationary Processes and Long Memory Processes.

5. Determining the Value at Risk and Expected Shortfall by taking recourse to the Theory of Extremes.

6. State Space Models and Kalman Filter.

7. Stochastic Volatility.

8. Other topics: the relation between several financial series (copulas, VAR, cointegration); method of moments; high frequency data.

Head Lecturer(s)

Rui Armando Pardal Silva Pascoal

Assessment Methods

Assessment
Research work: 50.0%
Mini Tests: 50.0%

Bibliography

J.Y. Campbell, A.W. Lo, A.C. Mackinlay, The Econometrics of Financial Markets, Princeton University Press, Princeton, New Jersey, 1997.

J.D. Hamilton, Time Series Analysis, Princeton University Press, Princeton, 1994.

P. Embrechts,  C. Kloppelberg,  T. Mikosch, Modelling Extremal Events, Springer-Verlag, Berlin, 1997.

R.S. Tsay, Analysis of Financial Time Series, Wiley, New York, 2002.

E. Zivot, Jiahui Wang,  Modelling Financial Time Series with SPLUS, Springer-Verlag, 2005.

J. Beran, Statistics for Long-Memory Processes, Chapman & Hall, 1994.

J. Durbin, S.J. Koopman, Time Series Analysis by State Space Methods, Oxford University Press, 2001