Financial Econometrics

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  • Level of Module/Course (under-/postgraduate):

  • Type of Module/Course:


PC (Prescribed Core Module)

PS (Prescribed Stream Module)

ES (Elective Stream Module)

E (Elective Module)


  • Year of Study


  • Semester


  • Number of ects allocated:


  • Νumber of teaching units:


    Name of lecturer / lecturers :

  • Content outline ( 100-300words) :

OLS (with one and many variables): assumptions, estimation, hypothesis testing, significance testing, linear restrictions testing, R2, adjusted R2, properties of estimators (unbiased, consistent), applications.

Hypotheses violations: autocorrelation, heteroskedasticity, statistical testing (White, Durbin-Watson, Breusch-Godfrey), GLS and FGLS estimators, correlation of error term and explanatory variables, multicollinearity, misspesification.

Other OLS related subjects: stability tests, dynamic models

Time series models: Autoregressive models (AR), moving average models (MA), Box-Jenkins methodology and ARMA models, forecasting, non stationary processes, unit root tests, cointegration and error correction models, ARIMA models, volatility models (ARCH, GARCH).

All subjects covered are combined with financial applications that are familiar to the students from other courses.

  • Learning outcomes (200-500 words) :

The main targets of the course are:

1. To let students come into contact with an object that although applied in nature, is based on theoretical knowledge of mathematics and statistics gained by the students in earlier stages of their studies. The objective in this regard is for the students to further absorb older knowledge and realize the close relation between theory and applications.
2. To introduce students to the basic theory of econometrics, thus connecting the main theoretical results in econometrics with the assumptions underlying these results and the limitations involved in the way we use econometrics in applications. The aim is for the students to understand that an econometric technique can be useful only provided that we understand well, both the related econometric theory and the specific problem to which we implement it.
3. Help students acquire skills in using computer programs (Eviews, excel) to perform econometric applications.
4. Facilitate the understanding of how to apply econometrics to problems arising in economic theory and practice, especially so in the field of finance. All students are expected to be able to use the knowledge acquired in the course, to problems similar to those discussed in the lectures, while talented students are expected to be able to cope also with problems that are new to them.
5. Help students obtain a better understanding of the problems and limitations that accompany the commercial financial applications (risk management applications etc.) based on econometrics, in order to use them properly and treat their results and projections with the appropriate caution.
6. Motivate students to further their involvement with econometrics.

  • Prerequisites (max 50 words) :

Basic knowledge of mathematical calculus (one and many variables): derivatives, integrals, maximization with and without constraints.
Basic knowledge of linear algebra: matrix algebra (inverse, transpose, solving linear systems, differentiation with matrices, linear independence).
Basic knowledge of statistics: probability density function (simple and multivariate) cumulative probability distribution, moments, conditional distribution, expected values​​, etc.

  • Recommended Reading :

a) BasicTextbooks (up to 3) :

Brooks, Ch. (2002) “Introductory Econometrics for Finance”, CambridgeUniversity Press.

Χρήστου Γ (2004) «Εισαγωγή στην Οικονομετρία: Τόμοι Ι και ΙΙ», Gutenberg, β’ έκδοση.

Χάλκος, Γ .

b)Additional References (up to 10) :

Χάλκος, Γ (2007) «Οικονομετρία. Θεωρία και Πράξη: Οδηγίες χρήσης σε Eviews, Minitab, SPSS & Excel», Γκιούρας, Αθήνα.

Enders, W. (1995) “Applied Econometric Analysis”, John Wiley & Sons

Alexander, C. (2009) “Market Risk Analysis”, Wiley, 4 Volume Boxset.

  • Learning Activities and Teaching Methods (max 100 words) :

1. 10 lectures delivered with the help of power point presentations.
2. Teaching theory was combined with relevant financial applications discussed with the help of computer software (excel, eviews)
3. 9 sets of exercises were handed to the students during the course. They were solved by the students, corrected by the instructor and returned to the students with comments. In many cases the exercises required the use of appropriate computer software.

  • Assessment/Grading Methods (max 100 words) :

A three-hour final exam paper and extra credit (1,5/10) awarded for solving the exercises handed to the students during the course.

  • Language of Instruction :


  • Μ ode of delivery (face-to-face, distance learning):

Face to face lectures

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