Financial Econometrics with Applications in R
Full Time// 2nd Semester, Course Code: ΜΕΧΡΗ332
Part Time // 3rd Semester, Course Code: ΜΕΧΡΗ-Μ332
Credits: 6
Learning Outcomes
Upon successful completion of the course, the student
(a) will know the fundamental notions and classes of Stochastic Processes
(b) will have obtained understanding of the ARIMA formulation, will be able to identify patterns of autocorrelation and classify a timeseries applying the appropriate functions of the R programming language
(c) will be able to identify and apply unit root tests using the appropriate functions in R
(d) will have obtained understanding of the notion of cointegration, and will be able to test for the existence of cointegrating relationships using Johansen’s approach and the corresponding functions in R.
(e) will be able to test for heteroskedasticity and autocorrelation, and correct the standard errors of the estimators of a linear regression’s coefficients using the appropriate libraries and functions in R,
(f) will have a basic knowledge of the ARCH and GARCH models, and will be able to estimate their parameters using the corresponding functions in R.
General Competences
Within the framework of the combined skills that the graduate will acquire by attending all the courses of the study program, this course aims at the graduate to acquire abilities:
(a) in the search for, analysis and synthesis of data and information, with the use of the necessary technology,
(b) in decision-making
(c) in working independently
(d) to promote free, creative and inductive thinking
(e) in exercising criticism and self-criticism
Course Content
1) Introduction to the Theory of Stochastic Processes
Modeling time series data
Stationary Stochastic Processes
Autocorrelation and Partial Autocorrelation Function
Time series in R, time series plots, Autocorrelation and Partial Autocorrelation functions in R
Moving Average Processes
Autoregressive Processes
ARMA Processes
Stationarity Conditions
Stationary ARMA simulations in R
Testing for Autocorrelation and Heteroscedasticity by means of the appropriate R functions
The LAG operator
2) Unit Root Processes
Departure from Stationarity
Stochastic trends and unit roots
Unit Root Testing with Applications in R
ARIMA representation
ARIMA model estimation and simulations in R
3) Cointegration
The concept of Cointegration
Testing with the Engle-Granger method
Testing with the Johansen method
Libraries and functions in R for cointegration testing
4) Examples of Conditional Variance modeling
Engle’s ARCH model
The GARCH model of T. Bollerslev
Stationarity conditions
Variations and estimation using real data and the appropriate R functions
5) Introduction to Brownian Motion
Continuous time Processes
Properties of Brownian Motion
Geometric Brownian Motion
Link to financial data
Student Performance Evaluation
Formative and conclusive evaluation is carried out.
The final evaluation of the students is done by an oral exam on a take-home assignment and a written exam. The assignment contains the application of the concepts taught using data. The written exam is based on problem solving, short answer questions and development questions.
It is based on problem solving, short-answer questions and open-ended questions.
Bibliography
Suggested Bibliography
1) Tutor’s Notes
2) Modern Methods of Time Series Analysis (in Greek). S. Dimeli. AUEB Publications
3) Financial Econometrics (in Greek). A. Demos. AUEB Publications
4) Campbell Lo and MacKinlay,(1997) The Econometrics of Financial Markets, Princeton
5) Analysis of Financial Time Series, 2nd Edition. Tsay, Ruey, John Wiley & Sons
Related Academic Journals
- Journal of Financial Econometrics,
- Econometric Reviews,
- Econometric Theory,
- Journal of Econometrics,
- Journal of Applied Econometrics,
- Journal of Empirical Finance
Master of Science (M.Sc.) in
«Banking and Finance»
Specialization in
«Banking and Financial Management»