Advanced Financial Econometrics

Program Type:

Thesis

Course Code:

FE 602

P:

3

Lab:

0

Credits:

3

ECTS:

10

Course Language:

English

Course Objectives:

Other courses in the program are the theoretical and practical teaching of econometric information and models that are required to be used frequently in thesis writing and academic studies.

Course Content:

Basic financial time series models; Value-at-Risk (VaR) metrics tests and comparisons, stochastic volatility; Overview of AR, MA, ARMA, VaR dynamic models and predictions with ARIMA, VaR models; ARCH and GARCH Models and applications; Efficient Market Hypothesis and predictability of returns on financial assets; Testing of least squares and maximum likelihood methods, index models, CAPM and Multi-Factor Models. Autocorrelation Analysis.

Teaching Methods:

1: Lecture, 2: Question-Answer, 3: Discussion, 4: Simulation, 5: Case Study

Assessment Methods:

A: Testing, B: Experiment, C: Homework, Q: Quiz

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	Econometric analysis, William H. Greene, Prentice Hall, 5. Edition, 2003 Introductory Econometrics for Finance, C. Brooks, 2nd Edition
Additional Resources

Material Sharing

MATERIAL SHARING
Documents			Guidelines and additional examples for Lecture Topics and Homework Assignments
Assignments			Homework assignments
Exams			Midterm Exam and Final Exam

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

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-Term	1	30
Class Performance	1	30
Final Exam	1	40
	Total	100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
	Total	100

COURSE CATEGORY	Expertise/Field Courses

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