FACULTY OF BUSINESS
Department of International Trade and Finance
SOCIAL MEDIA
ITF 304 | Course Introduction and Application Information
| Course Name |
Quantitative Methods in Finance
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
ITF 304
|
Fall/Spring
|
3
|
0
|
3
|
6
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
First Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | DiscussionLecture / Presentation | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | ||||||
| Course Objectives | The aim of this lecture is to provide students with an understanding of the quantitative methods for finance and investment. This includes the ability to formulate problems into quantitative models, to aid the successful resolution of the problem. Using output from mathematical and statistical models, students will learn to analyze, interpret and derive potential outcomes from quantitative information. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This lecture guides the students through a wide array of mathematics, ranging from elementary basic mathematics, limit, derivative and integral, linear algebra and differential calculus to optimization and linear regression. These quantitative methods are illustrated with a rich and captivating assortment of applications to the analysis of portfolios, derivatives, exchange, fixed income securities and equities. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Introduction and Overview: The Importance of Mathematics in Finance | |
| 2 | Financial Time Series and Their Features | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 3 | Basic Statistics | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 4 | Linear Relationship in Time Series analysis Classical Linear Regression Model (CLRM) | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 5 | Linear Time Series Analysis CLRM Assumptions | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 6 | Project | - |
| 7 | Univariate Time Series Modelling | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 8 | Univariate Time Series Modelling | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 9 | Project | |
| 10 | Multivariate Models | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 11 | Multivariate Models | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 12 | Modelling Long Run Relationships in Finance | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 13 | Modelling Long Run Relationships in Finance | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 14 | Modelling Volatility | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 15 | Modelling Volatility | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007 |
| 16 | Final Exam |
| Course Notes/Textbooks | Rachev, S., Mittnik, S., Fabozzi, F., Focardi, S. and Jasić, T., Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons, 2007, ISBN: 978-0-471-78450-0 |
| Suggested Readings/Materials | James H. Stock and Mark W. Watson, Introduction to Econometrics, Pearson Education, 2003, ISBN: 978-9352863501 Marek, Capiński, Mathematics for finance: an introduction to financial engineering, Springer, 2003, 0857290819 Alpha C., Chiang, Fundamental methods of mathematical economics, Auckland : McGrawHill, 1983, 3rd edition. ISBN:9780070108134 Statistics and Business Economics by P. Newbold W. L. Carlson, B. Thorne, 8/e, Prentice Hall, ISBN:9780132745659 Zvi Bodie, Alex Kane, Alan J. Marcus, “Essentials of Investment”, 2010, ISBN:9781259354977 John L. Teall and Iftekhar Hasan, “Quantitative Methods for Finance and Investments, 2002, ISBN: 9780631223399 |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project |
2
|
40
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm | ||
| Final Exam |
1
|
60
|
| Total |
| Weighting of Semester Activities on the Final Grade |
2
|
40
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
60
|
| 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 |
14
|
3
|
42
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
2
|
20
|
40
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
0
|
||
| Final Exam |
1
|
45
|
45
|
| Total |
175
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To be able to identify and analyze problems in the field of trade and finance, and to develop solutions. |
X | ||||
| 2 | To be able to use the theoretical and practical knowledge gained in the field of International Trade and Finance. | |||||
| 3 | To be able to analyze the developments in global markets by using critical thinking skills. | X | ||||
| 4 | To be able to analyze and interpret data in the field of finance, commerce and economics by using information technologies effectively. | |||||
| 5 | To be able to acquire knowledge about the legal regulations and practices in the field. | |||||
| 6 | To be able to foresee and define the risks that could be encountered in the field of trade and finance and to take decisions to manage such risks. | |||||
| 7 | To be able to acquire and use verbal and numerical skills necessary for the nature of international trade and finance program. | X | ||||
| 8 | To be able to obtain, synthesize and report the information related to the fields of trade and finance. | |||||
| 9 | To be able to contribute to the solution of problems as individual, team member or leader. | |||||
| 10 | To be able to evaluate the issues related to the field with an ethical perspective and social sensitivity. |
|||||
| 11 | To be able to collect data in the areas of International Trade and Finance and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1). | |||||
| 12 | To be able to speak a second foreign at a medium level of fluency efficiently. | |||||
| 13 | To be able to relate the knowledge accumulated throughout human history to their field of expertise. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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