Financial Economics

Program Type:

Thesis

Non Thesis

Course Code:

FE 502

Semester:

Autumn

P:

3

Lab:

0

Credits:

3

ECTS:

10

Course Language:

English

Course Objectives:

To pass the information on the econometric models theoretically and empirically, providing the required material needed for the other courses, thesis and all academic studies.

Course Content:

Basic financial time series modeling extended to advanced topics on stochastic volatility, testing and comparing Value-at-Risk (VaR) measures and fixed income econometrics; overview of dynamic models AR, MA, ARMA, VAR and forecasting with ARIMA and VAR models; applications of Arch and Garch models in forex and stock returns; efficient market hypothesis and predictability of asset returns.

Teaching Methods:

1: Anlatım, 2: Soru-Cevap, 3: Tartışma

Assessment Methods:

A: Testing, C: Homework

Vertical Tabs

Course Learning Outcomes

Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
Coverage of Regression models, estimations and problems in regressions		1,2,3	A,C
Understanding and using econometric models that are frequently applied in Program courses		1,2,3	A,C
Learning basic financial time series models		1,2,3	A,C
Coverage of estimation, estimators and estimator properties in regression models		1,2,3	A,C
Detailed analysis of ARCH and GARCH models		1,2,3	A,C
Passing the theoretical and empirical econometrics information needed for thesis writing		1,2,3	A,C

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction	Statistics, probability review
2	The Nature of Regression Analysis: Single-Equation Regression Model	Univariate Regression analysis
3	Two-Variable Regression Analysis	Bivariate regression analysis
4	Two-Variable Regression Analysis: Estimation
5	Normality Assumption in Regression Analysis	Normality assumption
6	Exam I
7	Interval Estimation and Hypothesis Testing	Hypotesis tests
8	ANOVA, Analysis of Variance	Variance
9	Financial Econometric Applications (CAPM Model, and similar)	CAPM model
10	Multiple Regression Models and Financial Markets	Multivariate regression analysis
11	Problems in Regression Models: Multicollinearity, Heteroskedasticity, Autocorrelation	Multicollinearity
12	Problems in Regression Models: Multicollinearity, Heteroskedasticity, Autocorrelation (continue)	Heteroskedasticity
13	Problems in Regression Models: Multicollinearity, Heteroskedasticity, Autocorrelation (continue)	Autocorrelation
14	Final
15	More Applications in Financial Markets
16	Presentations of the (Small) Projects

Recommended Sources

RECOMMENDED SOURCES
Textbook	Basic Econometrics, D. Gujarati, 5th Edition Introductory Econometrics for Finance, C. Brooks, 2nd Edition
Additional Resources

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-terms	2	70
Quizzes
Assignment	6	30
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		30
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		70
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM
No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1	To use the information and skills gathered from the subjects of finance, economics, statistics and computer science, in interdisciplinary studies; to produce new application areas			X
2	With the awareness of learning and questioning lifetime, he/she follows (inter)national publications; is expected to enlarge the borders of information by producing academic, scientific papers.				X
3	He/she plans and executes analytical, modelling and experimental based research studies; solves problems and interprets results and makes estimations.					X
4	As a graduate, he/she is expected to collate the knowledge, characteristics and abilities and skills into his/her professional career.		X
5	With the proficiency in English, following the recent information and developments on international basis.				X
6	Widely uses and benefits from the software programs, data processing and information technologies required in the relevant fields.				X

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	4	64
Mid-terms	2	20	40
Ödev	6	12	72
Final examination	1	30	30
Total Work Load			254
Total Work Load / 25 (h)			10,16
ECTS Credit of the Course			10

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