COURSE UNIT TITLE

: FINANCIAL ECONOMETRIC MODELS AND INTERPRETATION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IKT 6042 FINANCIAL ECONOMETRIC MODELS AND INTERPRETATION ELECTIVE 3 0 0 8

Offered By

Economics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR HAKAN KAHYAOĞLU

Offered to

Economics

Course Objective

The aim of this course is to teach the theoretical structure of the basic econometric methods and techniques who plan to use research on Finance, Financial Econometrics and Macroeconomics and to provide this structure to carry on the practice

Learning Outcomes of the Course Unit

1   To be able to understand the structure and properties of high frequency time series
2   To be able to learn the basic information for the prediction of the volatility parameters
3   To be able to interpret the results
4   To be able to make the analysis of nonlinear time series.
5   To be able to learn how to monitor developments in the field and learn programs to be used in non-linear time series analysis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Importance of econometric methods on financial instruments Prices analyze. The properties of High frequency and Financial time series.
2 Financial and Statistical Distributions, ARMA Modeling Process, Box-Jenkins Approach, ARMA structure. Correctional Exponential Models and Application
3 Volatility Prediction Approaches (different approaches to estimate the volatility of prices of financial instruments, Delayed volatility models: GARCH
4 Volatility Measurement approaches: EGARCH, GJR, APARCH, IGARCH, Risk Metrics, FIGARCH, FIEGARCH, FIAPARCH, HYGARCH.
5 Volatility Measurement approaches: EGARCH, GJR, APARCH, IGARCH, Risk Metrics, FIGARCH, FIEGARCH, FIAPARCH, HYGARCH.
6 Volatility Measurement approaches: EGARCH, GJR, APARCH, IGARCH, Risk Metrics, FIGARCH, FIEGARCH, FIAPARCH, HYGARCH.
7 The effects of the shocks and the variance breaks on Financial Time Series
8 Multivariate volatility models
9 Multivariate volatility models
10 Mid-term Exam
11 Nonlinearity feature in Time Series and New Approaches: Threshold and artificial neural network approaches , Markow Regime Change Modeling and According to Time Variable Parameter Models and Approaches
12 Applications for Nonlinear Time Series: Threshold and Transition transition prediction methods and artificial network
13 Non-linear cointegration analysis of time series.
14 Non-linear cointegration analysis of time series.

Recomended or Required Reading

Laura, Chihara, and Tim Hesterberg. Mathematical Statistics with Resampling and R. Wiley, 2011.
Manfred, Gilli, , Dietmar Maringer, and Enrico Schumann. Numerical Methods and Optimization in Finance. Academic Press, 2011
Vasishth, Shravan and Michael Broe. The Foundations of Statistics: A Simulation-based Approach. Springer, 2010.
Muenchen, Robert A. and Joseph M. Hilbe. R for Stata Users. Statistics and Computing. Springer, 2010.
Quick, John M.. The Statistical Analysis with R Beginners Guide. Packt Publishing, 2010.
Ruppert, David. Statistics and Data Analysis for Financial Engineering. Use R! Springer, 2010.
Robert, Christian and George Casella. Introducing Monte Carlo Methods with R. Use R. Springer, 2010.
Vinod, Hrishikesh D., editor. Advances in Social Science Research Using R. Lecture Notes in Statistics. Springer, 2010.

Planned Learning Activities and Teaching Methods

the method of course will be made towards the establishment of a theoretical infrastructure that provide for Applications and aim to use relevant literature in investigation about financial economics or finance. Therefore, the course will be based on different types of applications implemented using econometric schedule.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.20 + STT* 0.40 + FIN* 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.40 + RST* 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

hakan.kahyaoglu@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 30 30
Preparation for final exam 1 40 40
Preparing assignments 1 30 30
Reading 1 15 15
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 197

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.155
LO.2555555
LO.3555
LO.45555
LO.5555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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