FACULTY OF ARTS AND SCIENCES
Department of Mathematics
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
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
First Cycle
|
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| 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 | ||
| Supportive Courses |
X
|
|
| 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 have a grasp of basic mathematics, applied mathematics or theories and applications of statistics. |
|||||
| 2 | To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics. |
X | ||||
| 3 | To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials. |
|||||
| 4 | To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning. |
X | ||||
| 5 | To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals. |
X | ||||
| 6 | To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project. |
|||||
| 7 | To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge. |
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| 8 | To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field. |
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| 9 | To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security. |
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| 10 | To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking. |
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| 11 | To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language. |
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| 12 | To be able to speak a second foreign language at a medium level of fluency efficiently. |
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| 13 | To be able to relate the knowledge accumulated throughout the human history to their field of expertise. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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