ITF 415 | Course Introduction and Application Information - Department of International Trade and Finance

Course Name

Financial Econometrics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
ITF 415	Fall/Spring	3	0	3	6

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Prof. Dr. Gülin VARDAR
Course Lecturer(s)	Prof. Dr. Gülin VARDAR
Assistant(s)	-

Course Objectives	The primary objective is to introduce and teach a broad knowledge of modern econometric techniques commonly employed in the finance literature. Although seems a demanding quantitative background, to enhance the learning outcome, all examples can easily be applied through several software such as Eviews and RATS. The course can be utilized to support other disciplines such as financial economics, securities and investments. Finally the course presents a unique opportunity to better understand the sophisticated financial markets and provide students with skills to estimate and interpret models while developing intuitive grasp of underlying theoretical concepts.
Learning Outcomes	The students who succeeded in this course; will be able to define classical linear regression model with the significance and hypothesis testing. will be able to analyze classical linear regression model in advance level and multiple regression models in econometric software. will be able to determine the difference between assumptions of classical linear regression model and Durbin Watson and Godfrey autocorrelation tests. will be able to identify the univariate time series modelling, the approriate time series models for a given data series and the characteristics of various types of stochastic process will be able to describe multivariate models with Granger causality tests and VARs in econometric software in order to explain relative advantages and disadvantages of them. will be able to estimate forecasts from GARCH models and ARCH effects in time series data. will be able to explain the difference between pure simulation, bootstrapping and Monte Carlo simulation which are main simulation methods. will be able to explain the basic features of financial data and econometrics by distinguishing different types of data and creating an econometric model to calculate the return on assets.
Course Description	Topics covered are: definition of econometrics and financial econometrics, classical linear regression model and its assumptions, univariate and multivariate models, cointegration models, modeling volatility and correlation and simulation models.
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Introduction
2	Stylized facts regarding financial data and econometricsa) Distinguish between different types of data b) Describe the steps involved in building an econometric model c) Calculate asset price returns and accomplish simple tasks in econometric software	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
3	Klasik doğrusal regresyon modelia) Parametrelerin ve standart hatalarının tahminini yapabilmek için EKKY formülünün türetilmesib) Anlamlılık testi ve güven aralığı yaklaşımları ile hipotez testleric) Ekonometri paketlerinde regresyon modellerinin tahmini ve tek hipotezi testi.	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
4	Overview of the classical linear regression model a) Derive OLS formulae for estimating parameters and their standard errors. b) Test hypothesis using the test of significance and confidence interval approaches c) Estimate regression models and test single hypotheses in econometric software.	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
5	Further development and analysis of the classical linear regression model.a) Construct models with more than one explanatory variable b) Test multiple hypotheses using an Ftest and determine how well a model fits the datac) Estimate multiple regression models and test multiple hypotheses in econometric software.	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
6	Classical linear regression model assumptions and diagnostic tests a) Describe the steps involved in testing regression residuals for heteroscedasticity and autocorrelation b) Distinguish between the DurbinWatson and Breusch—Godfrey tests for autocorrelation c) Determine whether the residual distribution from a regression differs significantly from normality and investigate whether the model parameters are stable	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
7	Univariate time series modelling and forecasting a) Explain the defining characteristics of various types of stochastic processes b) Identify the appropriate time series model for a given data series and produce forecasts for ARMA and exponential smoothing models c)Evaluate the accuracy of predictions using various metrics and estimate time series models and produce forecasts from them in econometric software	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
8	Univariate time series modelling and forecasting a) Explain the defining characteristics of various types of stochastic processes b) Identify the appropriate time series model for a given data series and produce forecasts for ARMA and exponential smoothing models c)Evaluate the accuracy of predictions using various metrics and estimate time series models and produce forecasts from them in econometric software	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
9	Article Presentation
10	Project topic selection
11	Multivariate modelsa) Describe several methods for estimating simultaneous equations models and explain the relative advantages and disadvantages of VAR modelingb) Estimate optimal lag lengths, impulse responses and variance decompositions c) Conduct Granger causality tests and construct simultaneous equations models and VARs in econometric software	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
12	Modelling volatility and correlationa) Discuss the features of data that motivate the use of GARCH models and explain how conditional volatility models are estimatedb) Test for ‘ARCHeffects’ in time series datac) Produce forecasts from GARCH models d) Estimate univariate and multivariate GARCH models in econometric software using maximum likelihood	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
13	Modelling volatility and correlationa) Discuss the features of data that motivate the use of GARCH models and explain how conditional volatility models are estimatedb) Test for ‘ARCHeffects’ in time series datac) Produce forecasts from GARCH models d) Estimate univariate and multivariate GARCH models in econometric software using maximum likelihood	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
14	Simulation methodsa) Design simulation frameworks to solve a variety of problems in financeb) Explain the difference between pure simulation and bootstrappingc) Monte Carlo simulation d) Implement a simulation analysis in econometric software	Chris Brooks, “Introductory Econometrics for Finance”, Second Edition
15	Project submission
16	Final Exam

Course Notes/Textbooks

Chris Brooks, “Introductory Econometrics for Finance”, Second Edition, Cambridge University Press, ISBN: 978-0-521-69468-1

Book Chapters and Powerpoint slides

Suggested Readings/Materials

Journal of Financial Econometrics

Journal of Econometrics

Journal of Applied Econometrics

Econometric Reviews

Journal of Empirical Finance

Financial Times

Wall Street Journal

Semester Activities	Number	Weigthing
Participation	1	10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	1	20
Presentation / Jury	1	30
Project
Seminar / Workshop
Oral Exams
Midterm
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	3	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	16	3	48
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	1	20	20
Presentation / Jury	1	22	22
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam	1	30	30
		Total	168

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	To be able to identify and analyze problems in the field of trade and finance, and to develop solutions.	-	-	-	-	-
2	To be able to use the theoretical and practical knowledge gained in the field of International Trade and Finance.	-	-	-	-	-
3	To be able to analyze the developments in global markets by using critical thinking skills.	-	-	-	-	X
4	To be able to analyze and interpret data in the field of finance, commerce and economics by using information technologies effectively.	-	-	-	-	X
5	To be able to acquire knowledge about the legal regulations and practices in the field.	-	-	-	-	-
6	To be able to foresee and define the risks that could be encountered in the field of trade and finance and to take decisions to manage such risks.	-	-	-	-	-
7	To be able to acquire and use verbal and numerical skills necessary for the nature of international trade and finance program.	-	X	-	-	-
8	To be able to obtain, synthesize and report the information related to the fields of trade and finance.	-	X	-	-	-
9	To be able to contribute to the solution of problems as individual, team member or leader.	-	-	-	-	-
10	To be able to evaluate the issues related to the field with an ethical perspective and social sensitivity.	-	-	-	-	-
11	To be able to collect data in the areas of International Trade and Finance and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1).	-	-	-	-	-
12	To be able to speak a second foreign at a medium level of fluency efficiently.	-	-	-	-	-
13	To be able to relate the knowledge accumulated throughout human history to their field of expertise.	-	-	-	-	-