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 3 0 0 3 6
Pre-requisite Course(s)
Math 231 and Math 392
Course Language English
Course Type N/A
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
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;
  • be able to measure of Statistical Measures on Return and Volatility of financial assets
  • learn on the theoritical base of financial measures
  • be able to compute and interpret the correlations between more than one asset
  • be able to construct simple/multi regression model and analyse them
  • be able to predict the return value of any financial asset for near future
  • understand on evaluation methots on various portfolios
  • be able to construct model by Moving Average Methot on various time series.
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


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)
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration
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