COURSE UNIT TITLE

: MULTIVARIATE DATA ANALYSIS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DIŞ 5034 MULTIVARIATE DATA ANALYSIS ELECTIVE 3 0 0 6

Offered By

Foreign Trade (English)

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR BERNA KIRKULAK ULUDAĞ

Offered to

Foreign Trade (English)

Course Objective

This course focuses on the financial time series models empirically. Statistical and econometrical features of aforementioned models, their estimation processes, and interpretation of model results will be discussed at introductory level in the course.

Learning Outcomes of the Course Unit

1   Use quantitative methods for analyzing high frequency financial data.
2   Understand basic properties of fundamental statistical and econometric models
3   Use basic financial data analysis software to estimate models and interpret the estimation results.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Financial Time Series and Their Features
2 Basic Statistics
3 Linear Relationship in Time Series analysis Classical Linear Regression Model (CLRM)
4 Linear Time Series Analysis CLRM Assumptions
5 Linear Time Series Analysis CLRM Assumptions
6 Midterm Exam
7 Univariate Time Series Modelling
8 Multivariate Models
9 Multivariate Models
10 Modelling Long Run Relationships in Finance
11 Modelling Long Run Relationships in Finance
12 Modelling Volatility
13 Modelling Volatility
14 Final Exam

Recomended or Required Reading

1. Analysis of Financial Data, Gary Koop, Wiley Publishing.

Planned Learning Activities and Teaching Methods

The course consists of lectures, class discussions, computer applications

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FCG FINAL COURSE GRADE
3 FCGR FINAL COURSE GRADE MTE * 0.40 + FCG* 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST* 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

1. The learner will show understanding the basic statistical and properties of fundamental financial data analysis models.
2. The learner will model financial data through computer programs and interpret the results.

Language of Instruction

English

Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.
2. Violations of Plagiarism of any kind will result in disciplinary steps being taken.

Contact Details for the Lecturer(s)

sabri.erdem@deu.edu.tr, aysun.kapucugil@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 25 25
Preparation for final exam 1 35 35
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 142

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555
LO.255
LO.3555