Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
7ECON 435Forecasting in Financial Markets3+0+035

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program BA Program in Economics
Mode of Delivery Face to Face
Type of Course Unit Elective
Objectives of the Course Course description
This course will introduce students to some widely-used models used to study and forecast financial markets and familiarize them with the properties of financial data. The models to be covered include autoregressive and ARMA models, GARCH models for volatility forecasting, Value- at-Risk models, and models using high frequency (intra-day) asset prices
Course Content Assumed/required prior knowledge
Students will be expected to learn and apply the statistical software package EVIEWS to implement the models covered in class on real data. Previous knowledge of EVIEWS is not required.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Prof.Dr. Muhittin Kaplan
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Bodie, Z., A. Kane and A.J. Marcus, 2010, Investments, 9th Ed., McGraw-Hill, USA
Brooks, C., 2008, Introductory Econometrics for Finance, 2nd Ed., Cambridge University Press, Cambridge.
Campbell, J. Y., A.W. Lo, and A.C. MacKinlay, 1997, fhe Econometrics of Financial Mar- kets, Princeton University Press, Princeton, New Jersey.
John E. Hanke Dean Wichern (2014), Business Forecasting, Pearson Education Limited.
Sypros Makridakis, Steven C. Wheelwright and Rob J. Hyndman (1998) Forecasting: Methods and Applications, John Wiley and Sons, Inc.
Walter Enders (2015), Applied Econometrics Time Series, Wiley, USA.
I. Gusti Ngurah Agung, (2019) Advanced time series data analysis: Forecasting using EViews, John Wiley & Sons.

Course Category
Mathematics and Basic Sciences %0
Engineering %0
Engineering Design %0
Social Sciences %100
Education %0
Science %0
Health %0
Field %0

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 4 56
Assignments 4 6 24
Mid-terms 1 15 15
Final examination 1 15 15
Total Work Load   Number of ECTS Credits 5 152

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Review of statistics and econometrics (including linear regression and hypotheses tests)
2 Zaman serisi analizine giriş (otokorelasyonlar, AR, MA ve ARMA modelleri)
3 The efficient markets hypothesis and financial market predictability
4 Forecasting with ARMA models and model selection methods


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Business Forecasting
2 The forecast Process
3 Moving Averages and Exponential Smoothing
4 Decomposition Methods
5 Box-Jenkins Methodology: ARIMA Models
6 Forecasting with Simple Regression
7 Forecasting with multiple regression
8 Vector Autoregressive (VAR) Model
9 Modelling Volatility
10 Forecasting financial market volatility (ARCH-GARCH models)
11 Modelling financial market correlations (multivariate GARCH)
12 Cointegration and Error Correction Model


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 3
C1 3
C2 3
C3 3
C4 3

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://obs.ihu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=210032&lang=en