ECTS - Statistical Methods and Financial Applications
Statistical Methods and Financial Applications (MATH437) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Statistical Methods and Financial Applications | MATH437 | Area Elective | 3 | 0 | 0 | 3 | 6 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Question and Answer, Problem Solving. |
| Course Lecturer(s) |
|
| Course Objectives | The goal of this course is to provide students in mathematical finance programs with a basic background in statistical methods. The presentation of the course attempts to strike a balance between theory and applications. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Central tendency/dispersion measures, moments, maximum likelihood estimation, correlation and simple linear regression, multi-regression model, autocorrelation and multi-collinearity on regression models, portfolio theory, CAPM and ARMA approaches. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Role of statistical method in financial analysis, such as central tendency/dispersion measures of unclassified and classified data | [2] pp.254-262 |
| 2 | Moment of a random variable, skewness and kurtosis of distributions by moment | [2] pp. 266-270 |
| 3 | Lost function, Risk Function, Least Square and Maximum Likelihood Estimation of a parameter | [2] pp. 244-249 [2] pp. 267-270 |
| 4 | Pearson Correlation, sperman’s rank correlation Significance tests of correlation coefficient, confidence interval of correlation | [1] pp. 38-40 [2] pp. 383-390 |
| 5 | Simple Linear Regression Model, Estimation of Model Coefficients, Sum of Squared Error | [1] pp. 170-183 [2] pp. 359-376 |
| 6 | prediction of dependent variable, significance test of regression coefficient, confidence interval of predicted y value, determination coefficient | [1] pp.183-187 |
| 7 | Midterm | |
| 8 | Trend Analysis, Extrapolation of y value for time series | [1] pp.183-187 |
| 9 | Multi regression model, variance-covariance matrix, significance test of regression coefficients, ANOVA test | [1] pp. 192-206 |
| 10 | Multicollinearity, Autocorrelation, Von Neumann test | [1] pp. 187-219 |
| 11 | Portfolio Management, Expected Return and Volatility of portfolio | [1] pp. 138-143 |
| 12 | Risky asset and risk free asset, meaning of beta coefficient, SHARPE ,TREYNOR and JENSE indexs | [1] pp. 143-147 |
| 13 | CAPM approach, Market Line, Alfa value of any investment | [1] pp. 225-242 |
| 14 | Forecasting by Moving average method and ARMA model | [1] pp. 101-110 |
| 15 | Review | |
| 16 | Final |
Sources
| Course Book | 1. Statistics and Finance, An introduction, David Ruppert, Springer Texts in statistics. |
|---|---|
| 2. Mathematical Statistics, John E. Freund, Prentice/ Hall İnternational editions, Second edition | |
| Other Sources | 3. Methods and Applications of Statistics in Business, Finance and Management Science, N. Balakrishnan, Editor, Wiley Publication |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 2 | 10 |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 2 | 50 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 5 | 100 |
| Percentage of Semester Work | 60 |
|---|---|
| Percentage of Final Work | 40 |
| Total | 100 |
Course Category
| Core Courses | |
|---|---|
| Major Area Courses | X |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Has the ability to apply scientific knowledge gained in the undergraduate education and to expand and extend knowledge in the same or in a different area | X | ||||
| 2 | Can apply gained knowledge and problem solving abilities in inter-disciplinary research | X | ||||
| 3 | Has the ability to work independently within research area, to state the problem, to develop solution techniques, to solve the problem, to evaluate the obtained results and to apply them when necessary | X | ||||
| 4 | Takes responsibility individually and as a team member to improve systematic approaches to produce solutions in unexpected complicated situations related to the area of study | X | ||||
| 5 | Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework | X | ||||
| 6 | Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility | X | ||||
| 7 | Has the ability to follow recent developments within the area of research, to support research with scientific arguments and data, to communicate the information on the area of expertise in a systematically by means of written report and oral/visual presentation | X | ||||
| 8 | To have an oral and written communication ability in at least one of the common foreign languages ("European Language Portfolio Global Scale", Level B2) | X | ||||
| 9 | Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge | X | ||||
| 10 | Follows scientific, cultural, and ethical criteria in collecting, interpreting and announcing data in the research area and has the ability to teach. | X | ||||
| 11 | Has professional ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | |||
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 20 | 40 |
| Prepration of Final Exams/Final Jury | 1 | 40 | 40 |
| Total Workload | 128 | ||