Advanced Financial Time Series Analysis

Program Type:

Thesis

Course Code:

FE 612

P:

3

Lab:

0

Credits:

3

ECTS:

10

Course Language:

English

Course Objectives:

Giving theoretical and applied advanced econometrics and time series analysis, which are required to be used in other courses in the program, thesis writing and academic studies.

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: Anlatım, 2: Soru-Cevap, 3: Tartışma

Assessment Methods:

A: Sınav, B: Sunum C: Ödev, D: Proje

Vertical Tabs

Course Learning Outcomes

Course Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Method
Covering regression models, predictions and problems in regression		1,2,3	A,B,C,D
Review and understanding of econometric models		1,2,3	A,B,C,D
Teaching basic financial time series models		1,2,3	A,B,C,D
Processing forecasts, estimators and their properties in econometrics		1,2,3	A,B,C,D
Comprehensive coverage of ARCH and GARCH models		1,2,3	A,B,C,D
Giving the necessary theoretical and applied time series analysis information for thesis writing		1,2,3	A,B,C,D

Course Flow

COURSE CONTENT
WEEK	TOPICS	STUDY MATERIALS
1	Basic Concepts, Charting Tools, and Time Series Examples
2	Regression, Trend and Seasonality
3	Model Evaluation Criteria and Selection of the Appropriate Model
4	Stationary Models
5	Moving Average and Self Dependent Processes
6	Spectral Theory and Filtering
7	Non-Stationary Models
8	Midterm
9	Unit Root and Unlimited Time Series
10	Seasonal Patterns
11	Multivariate Time Series
12	State Space Models
13	Transfer Function Models
14	Nonlinear Models

Recommended Sources

RESOURCES
Textbook	Applied Time Series Modelling and Forecasting, John Wiley & Sons, England Brockwell, P.J. & Davis, R.A. (2002). Introduction to Time Series and Forecasting, 2nd edition, Springer, USA. Kendall, M.G. & Ord, J.K. (1990). Time Series, 3rd edition, Hodder Education.
Others Resources

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-Term	2	70
Assignment	6	30

	Total	100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		30
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		70
	Total	100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAMME
No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1	It uses the knowledge and skills it has absorbed in the fields of Economics, Finance, Statistics and Computer Science in interdisciplinary studies and produces different application areas.			X
2	With the awareness of lifelong learning and questioning, it follows national and international publications; It is expected to expand the limit of knowledge with scientific articles by reaching the level of preparing works in accordance with academic rules.				X
3	Designs, implements, solves and interprets analytical, modeling and empirical research; This way it makes predictions.					X
4	When she is involved in business life, she is expected to blend her knowledge in different fields with her differences and competencies and reflect them to her individual career.		X
5	Being aware of the ethical values, students know the individual, social and ecological dimensions of the concept of social responsibility and can prove that they understand the active citizenship duty that falls upon them within this framework.				X
6	Uses computer software and information and communication technologies required by related fields at an advanced level.			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: 15x Total course hours/week)	16	3	48
Hours for off-the-classroom study (Pre-study, practice, review/week)	16	4	64
Homework	6	12	72
Mid-term	2	20	40
Final	1	30	30
Total Work Load			254
Total Work Load / 25 (h)			10,16
ECTS Credit of the Course			10

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