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GRADUATE SCHOOL

Logistics Management (Without Thesis)

ECON 517 | Course Introduction and Application Information

Course Name

Financial Econometrics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
ECON 517	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
Course Coordinator	-
Course Lecturer(s)	-
Assistant(s)	-

Course Objectives	This course introduces the student to a wide range of techniques in financial econometrics, and their practical applications. Prior knowledge of statistics and econometrics is very useful, but it isn’t necessary. Each student is required to hand in a class project that applies class material to real financial data. Accordingly, one of the aims of the course is to give students the skills necessary to pursue independent research projects, and the backgrounds to be able to extend their knowledge to additional topics of interest without much difficulty. Class applications will utilize the open source econometrics software, Gretl. It can be downloaded and installed free of charge from the website: http://gretl.sourceforge.net/
Learning Outcomes	The students who succeeded in this course; will be able to identify the specific properties of financial data. will be able to use econometric tools that are specifically designed to analyze financial data. will be able to build econometric models that describe the behavior of financial time series. will be able to pursue an independent empirical research project from start to finish, including data collection, estimation, and interpretation of results. will be able to extend his/her knowledge on financial econometrics without much difficulty. will be able to seek answers to financial questions beyond the coverage of this class.
Course Description	The course will mostly be based on Time Series econometric methods. While this is the ideal approach for an introduction to the fundamental methods of quantitative finance, the student should keep in mind that the range of econometric methods that can be used to answer questions related to finance and financial economics spans almost the entire spectrum of econometrics. The course starts by reviewing basic tools of statistics and econometrics, and makes brief introductions to regression analysis, least squares methods, and some extensions of these topics. Then, numerous time series methods are discussed, including the estimation and forecasting of ARMA and ARIMA models, models of conditional heteroscedasticity (ARCH/GARCH), vector autoregressions, and cointegration. Each topic is discussed along with its applications in finance, keeping in mind the peculiarities of financial data and methods that are designed to work with such data.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation
1	Foundations: A Review of Probability and Statistics	Class notes.
2	Introduction to Regression Analysis	Brooks, Chapters 2 & 3
3	Topics in Regression Analysis	Brooks, Chapters 3 & 4
4	Foundations of Time Series Econometrics	Brooks, Chapter 5
5	ARMA Modeling, pt.1	Brooks, Chapter 5
6	ARMA Modeling, pt.2	Brooks, Chapter 5, Class notes and additional reading material
7	Midterm Exam
8	Nonstationarity, Unit Roots, and ARIMA Models	Brooks, Chapter 7
9	Forecasting with Time Series	Brooks, Chapter 5
10	Autoregressive Conditional Heteroscedasticity: ARCH and GARCH, pt. 1	Brooks, Chapter 8, Class notes and additional reading material
11	Autoregressive Conditional Heteroscedasticity: ARCH and GARCH, pt.2	Brooks, Chapter 8, Class notes and additional reading material
12	Stationary Vector Models: VAR	Brooks, Chapter 6
13	Cointegration and Common Trends	Brooks, Chapter 7
14	Additional Topic (Optional and Time Permitting)
15	Additional Topic (Optional and Time Permitting)
16	Review of the Semester

Course Notes/Textbooks	Svetlozar T. Rachev, Stefan Mittnik, Frank J. Fabozzi, Sergio M. Focardi, and Teo Jasic, Financial Econometrics: From Basics to Advanced Modeling Techniques (John Wiley & Sons, Inc.).
Suggested Readings/Materials	On Financial Econometrics: • Chris Brooks, Introductory Econometrics for Finance (Second Edition) • Carol Alexander, Market Models: A Guide to Financial Data Analysis. On Time Series: • Walter Enders, Applied Econometric Time Series (Second Edition) • Brockwell and Davis, Introduction to Time Series and Forecasting (Second Edition)

EVALUATION SYSTEM

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques	-	-
Portfolio
Homework / Assignments	5	20
Presentation / Jury
Project	2	55
Seminar / Workshop
Oral Exams
Midterm	1	25
Final Exam
Total

Weighting of Semester Activities on the Final Grade		75
Weighting of End-of-Semester Activities on the Final Grade		25
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class			0
Field Work			0
Quizzes / Studio Critiques	-		0
Portfolio			0
Homework / Assignments	5	9	45
Presentation / Jury			0
Project	2	46	92
Seminar / Workshop			0
Oral Exam			0
Midterms	1	40	40
Final Exam			0
		Total	225

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	Being able to contribute to the institution the participant works for and the logistics sector by the use of the knowledge and abilities gained during the education period; and manage change in the institution and the sector;
2	Reaching a competency about contemporary business and technology applications in the area of logistics and supply chain management and analysis and strategy development methods;
3	Being able to create opportunities by combining supply chain management with information technologies and innovative processes by the use of the interdisciplinary courses the participants take;
4	Having the ability to develop creative solutions by working on global logistics and supply chain subjects and realizing these by the use of their project management knowledge;
5	Having the knowledge, abilities and capabilities required for effective logistics and supply chain management by the use of a problem and case analysis based learning;
6	Being able to examine logistics and supply chain processes with the management science viewpoint, analyze related concepts and ideas by scientific methods;
7	If continuing to work in the academia, having the necessary information on logistics applications; if continuing to work in the sector, having the necessary knowledge on conceptual subjects;
8	Being able to specify appropriate research questions about his/her research area, conduct an effective research with the use of necessary methods and apply the research outcomes in the sector or the academia;
9	Being able to follow the changes and developments in the sector the participant works in, in order to keep his/her personal and professional competence updated and develop himself/herself when necessary;
10	Be experts in the fields of logistics and supply chain with the help of the sectorfocused education they receive;
11	Have the necessary capabilities to pursue doctoral studies in national and foreign institutions

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest