COURSE UNIT TITLE

: APPLIED FINANCIAL ECONOMETRICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DBA 6220 APPLIED FINANCIAL ECONOMETRICS ELECTIVE 3 0 0 6

Offered By

Business Administration (English)

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR EFE ÇAĞLAR ÇAĞLI

Offered to

Business Administration (English)

Course Objective

This course aims to provide students with the quantitative skill required to analyze financial problems effectively. The students will gain insight into the mathematical understanding of economic and financial theories, considering stylized facts of financial data through employing appropriate econometric models, including univariate time-series, multivariate, volatility, and limited dependent variable models.

Learning Outcomes of the Course Unit

1   Distinguish between different types of financial data, and summarize financial data
2   Evaluate the accuracy of forecasts produced by autoregressive moving average (ARMA) and exponential smoothing models using various metrics
3   Describe several methods for estimating simultaneous equations models and conduct Granger causality tests
4   Test for unit roots and estimate error correction and vector error correction models
5   Test for `ARCH-effects in time-series data and produce forecasts from GARCH models
6   Describe the key features of panel data; contrast the fixed effect and random effect approaches.
7   Interpret and evaluate logit and probit models

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Statistical Foundations and Data Analysis
2 Linear Regression Model Diagnostic Tests
3 Univariate Time-Series Modelling and Forecasting
4 Univariate Time-Series Modelling and Forecasting
5 Multivariate Models
6 Multivariate Models
7 Stationarity, Unit Root Testing, and Cointegration
8 Stationarity, Unit Root Testing, and Cointegration
9 Modeling Volatility
10 Modeling Volatility
11 Panel Data
12 Panel Data
13 Limited Dependent Variable Models
14 Limited Dependent Variable Models

Recomended or Required Reading

Brooks, C. Introductory Econometrics for Finance, Cambridge University Press, 2019.
Hill, R.C., Griffiths, W. E. and Lim, G. C., Principles of econometrics, 4th Ed., Wiley, 2011.
Alexander C., Market Risk Analysis, Volume II, Practical Financial Econometrics, Wiley, 2008.
Alexander C., Market Risk Analysis, Quantitative Methods in Finance, Wiley, 2008.
Alexander C., Market Risk Analysis, Value at Risk Models, Wiley, 2008.
Tsay R. S. Analysis of Financial Time Series, 3rd Ed. Wiley, 2010.

Planned Learning Activities and Teaching Methods

Discussion, assignment, presentation

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRS PRESENTATION
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + PRS * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + PRS * 0.30 + RST* 0.40


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

Further Notes About Assessment Methods

None

Assessment Criteria

Late submissions of assignments will not be accepted

Language of Instruction

English

Course Policies and Rules

1. Attending at least 70 percent of lectures is mandatory.
2. Plagiarism of any type will result in disciplinary action.
3. Students are expected to participate actively in class discussions.
4. Students are expected to attend classes on time.
5. Students must obey the time limits of their presentation.

Contact Details for the Lecturer(s)

Assoc. Prof. Dr. Efe Çağlar ÇAĞLI
E-mail: efe.cagli@deu.edu.tr

Office Hours

Appointments via e-mail.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Preparations before/after weekly lectures 12 3 36
Preparation for midterm exam 1 15 15
Preparation for final exam 1 20 20
Preparing assignments 12 3 36
Preparing presentations 12 1 12
Final 1 1,5 2
Midterm 1 1,5 2
TOTAL WORKLOAD (hours) 159

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5
LO.155445
LO.255445
LO.355445
LO.455445
LO.555445
LO.655445
LO.755445