Financial Time Series Analysis

Program Type:

Thesis

Non Thesis

Course Code:

FE 512

P:

3

Lab:

0

Credits:

0

ECTS:

10

Course Language:

English

Course Objectives:

Covering the necessary advanced econometrics and time series analysis both with theory and applications to maintain the required level for program courses and thesis.

Course Content:

Returns and their empirical characteristics; Linear time series models and their applications; Volatility modeling via conditional heteroscedastic models; Nonlinear models, neural networks and their applications; High-frequency data analysis, realized volatility, and market microstructure; Continuous-time diffusion models and Ito's Lemma; Value at Risk (VaR), stress test, extreme value analysis and quintiles; Multivariate models, factor models, and their applications; Multivariate conditional heteroscedastic models; Markov Chain Monte Carlo methods and their applications.

Teaching Methods:

1: Lecture, 2: Question-Answer, 3: Discussion

Assessment Methods:

A: Testing, C: Homework

Vertical Tabs

Course Learning Outcomes

Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
To cover regression models, estimations and problems in regressions		1,2,3	A,C
Overview and advanced understanding of econometric models		1,2,3	A,C
Learning the basic financial time series model		1,2,3	A,C
Analysis of estimation and estimators		1,2,3	A,C
Detailed study of ARCH and GARCH models		1,2,3	A,C
To pass the required theoretical and empirical information on time series		1,2,3	A,C

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Basic Concepts, Graphical Tools and Time Series Examples
2	Regression, Trend and Seasonality
3	Model Evaluation Criteria and Selection of Appropriate Model
4	Stationary Models
5	Moving Average and Self-Linked Processes
6	Spectral Theory and Filtering
7	Non-Stationary Models
8	Midterm Exam Week
9	Unit Root and Unlimited Time Series
10	Seasonal Models
11	Multivariate Time Series
12	State Space Models
13	Transfer Function Models
14	Nonlinear Models

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-terms	2	70
Quizzes
Assignment	6	30
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE	1	30
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		70
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM
No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1	By the use of genuine thinking and research, being able to develop and investigate the details of up to date and advanced knowledge and literature on the fields of finance and economics; to reach innovative and new definitions in specified fields			X
2	To have the skills of analysing and evaluating the information in multiple fields together; starting from these information, being competent in independently planning and carrying out scientific research studies				X
3	To have the skill of passing the knowledge to people, with theoretical and practical basis.			X
4	To produce scientific papers with individual or group work in various subjects including finance, economics, statistics and computer science; to publish in national and international refereed journals; to present in national and international meetings.			X
5	With the proficiency in English, following the recent information and developments on international basis.				X
6	Widely uses and benefits from the software programs, data processing and information technologies required in the relevant fields.				X

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	4	64
Mid-terms	2	20	40
Ödev
Final examination	6	12	72
Total Work Load	1	30	30
Total Work Load / 25 (h)			254
ECTS Credit of the Course			10,16

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