COURSE INFORMATION Course Title: QUANTITATIVE TECHNIQUES IN FINANCE

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BAF 314	B	6	3	0	0	3	6

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Agim Kukeli , by appointment
Second Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Classroom and Meeting Time:	A 126, Wednesday 14:45 - 17:30
Course Description:	This course will introduce students to econometric methods applied to financial data with the goal of providing them all the necessary tools to understand the main features of financial data. The course begins with an introduction to financial time series and an introduction to AR, ARMA, ARCH and GARCH models. It also introduces high frequency data, continuous time models, multivariate time series and provides methods for computing Value at Risk. By the end of the course the student should be able to apply these tools to real financial problems.
Course Objectives:	To provide students with an understanding of the quantitative techniques in Finance. To understand why statistics and econometrics are important for making decisions in Finance. To demonstrate a sound knowledge of related terms. To be able to perform statistical and econometrical analysis for the problems in real life finance.

COURSE OUTLINE

Week	Topics
1	Chapter 3: Simple Regression (Pg. 29 – 42) 1. Introduction to regression: the classical linear regression model (CLRM) 2. Why do we do regressions? 3. The classical linear regression model 4. The ordinary least squares (OLS) method of estimation 5. Alternative expressions for ˆ β 6. The assumptions of the CLRM 7. General 8. The assumptions 9. Violations of the assumptions 10. Properties of the OLS estimators 11. Linearity 12. Unbiasedness 13. Efficiency and BLUEness 14. Consistency
2	Chapter 3: Simple Regression (Pg. 43 – 52) 1. The overall goodness of fit 2. Problems associated with R2 3. Hypothesis testing and confidence intervals 4. Testing the significance of the OLS coefficients 5. Confidence intervals 6. How to estimate a simple regression in EViews and Stata 7. Simple regression in EViews 8. Simple regression in Stata 9. Reading the Stata simple regression results output 10. Reading the EViews simple regression results output 11. Presentation of regression results
3	Chapter 4 Multiple Regression (Pg. 65 – 74) 1. Derivation of multiple regression coefficients 2. The three-variable model 3. The k-variables case 4. Derivation of the coefficients with matrix algebra 5. The structure of the X_X and X_Y matrices 6. The assumptions of the multiple regression model 7. The variance–covariance matrix of the errors 8. Properties of multiple regression model OLS estimators 9. Linearity 10. Unbiasedness 11. Consistency 12. BLUEness 13. R2 and adjusted R2
4	Chapter 4 Multiple Regression (Pg. 75 – 87) 1. General criteria for model selection 2. Multiple regression estimation in EViews and Stata 3. Multiple regression in EViews 4. Multiple regression in Stata 5. Reading the EViews multiple regression results output 6. Hypothesis testing 7. Testing individual coefficients 8. Testing linear restrictions 9. The F-form of the likelihood Ratio test 10. Testing the joint significance of the Xs 11. F-test for overall significance in EViews 12. Adding or deleting explanatory variables 13. Omitted and redundant variables test in EViews 14. How to perform the Wald test in EViews 15. The t test (a special case of the Wald procedure) 16. The LM test 17. The LM test in EViews 18. Computer example: Wald, omitted and redundant variables tests 19. A Wald test of coefficient restrictions 20. A redundant variable test 21. An omitted variable test
5	Chapter 13: ARIMA Models and the Box–Jenkins Methodology (Pg. 265 – 274) 1. An introduction to time series econometrics 2. ARIMA models 3. Stationarity 4. Autoregressive time series models 5. The AR(1) model 6. The AR(p) model 7. Properties of the AR models 8. Moving average models 9. The MA(1) model 10. The MA(q) model 11. Invertibility in MA models 12. Properties of the MA models
6	Chapter 13: ARIMA Models and the Box–Jenkins Methodology (Pg. 275 – 284) 1. ARMA models 2. Integrated processes and the ARIMA models 3. An integrated series 4. Example of an ARIMA model 5. Box–Jenkins model selection 6. Identification 7. Estimation 8. Diagnostic checking 9. The Box–Jenkins approach step by step 10. Example: the Box–Jenkins approach 11. The Box–Jenkins approach in EViews 12. The Box–Jenkins approach in Stata
7	Chapter 14: Modeling the Variance: ARCH–GARCH Models (Pg. 287 – 299) 1. Introduction 2. The ARCH model 3. The ARCH(1) model 4. The ARCH(q) model 5. Testing for ARCH effects 6. Estimation of ARCH models by iteration 7. Estimating ARCH models in EViews 8. A more mathematical approach 9. The GARCH model 10. The GARCH(p, q) model 11. The GARCH(1,1) model as an infinite ARCH process
8	Midterm Examination
9	Chapter 14: Modeling the Variance: ARCH–GARCH Models (Pg. 300 – 313) 1. Estimating GARCH models in EViews 2. Alternative specifications 3. The GARCH in mean or GARCH-M model 4. Estimating GARCH-M models in EViews 5. The threshold GARCH (TGARCH) model 6. Estimating TGARCH models in EViews 7. The exponential GARCH (EGARCH) model 8. Estimating EGARCH models in EViews 9. Adding explanatory variables in the mean equation 10. Adding explanatory variables in the variance equation 11. Estimating ARCH/GARCH-type models in Stata 12. Empirical illustrations of ARCH/GARCH models
10	Chapter 15: Vector Autoregressive (VAR) Models and Causality Tests (Pg. 319 - 331 1. Vector autoregressive (VAR) models 2. The VAR model 3. Pros and cons of the VAR models 4. Causality tests 5. The Granger causality test 6. The Sims causality test 7. Computer example: financial development and economic growth, what is the causal relationship? 8. Estimating VAR models and causality tests in EViews and Stata 9. Estimating VAR models in EViews 10. Estimating VAR models in Stata
11	Chapter 16: Non-Stationarity and Unit-Root Tests (Pg. 334 – 350) 1. Introduction 2. Unit roots and spurious regressions 3. What is a unit root? 4. Spurious regressions 5. Explanation of the spurious regression problem 6. Testing for unit roots 7. Testing for the order of integration 8. The simple Dickey–Fuller (DF) test for unit roots 9. The augmented Dickey–Fuller (ADF) test for unit roots 10. The Phillips–Perron (PP) test 11. Unit-root tests in EViews and Stata 12. Performing unit-root tests in EViews 13. Performing unit-root tests in Stata
12	Chapter 17: Cointegration and Error-Correction Models (Pg. 355 – 363) 1. Introduction: what is cointegration? 2. Cointegration: a general approach 3. Cointegration: a more mathematical approach 4. Cointegration and the error-correction mechanism (ECM): a general approach 5. The problem 6. Cointegration (again) 7. The error-correction model (ECM) 8. Advantages of the ECM 9. Cointegration and the error-correction mechanism: a more mathematical approach 10. A simple model for only one lagged term of X and Y 11. A more general model for large numbers of lagged terms
13	Chapter 17: Cointegration and Error-Correction Models (Pg. 364 – 388) 1. Testing for cointegration 2. Cointegration in single equations: the Engle–Granger approach 3. Drawbacks of the EG approach 4. The EG approach in EViews and Stata 5. Cointegration in multiple equations and the Johansen approach 6. Advantages of the multiple-equation approach 7. The Johansen approach (again) 8. The steps of the Johansen approach in practice 9. The Johansen approach in EViews and Stata 10. Computer examples of cointegration 11. Monetization ratio 12. Turnover ratio 13. Claims and currency ratios 14. A model with more than one financial development proxy variable
14	Paper presentation Review for the final exam

Prerequisite(s):
Textbook:	Asteriou, D & Hall, S. G., (2011), Applied Econometrics, Second Edition, Palgrave Macmillan, UK
Other References:	Tsay R.S (2010). Analysis of Financial Time Series 3rd Edition, Wiley
Laboratory Work:
Computer Usage:	EVIEWS
Others:	No

COURSE LEARNING OUTCOMES

1	The knowledge and understanding of the unique aspects of financial time series.
2	To understand and evaluate the factors involved in currency exchange rates and stock markets (which influences international costs and profits) and economic conditions.
3	To analyze relationships in local and international financial markets.
4	To be able to make an accurate prediction using financial data
5	To master the use of EVIEWS

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Bachelor in Banking and Finance (3 years) Program
1	The students gain the ability to look at the problems of daily life from a broader perspective with an increased awareness of the importance of moral/ethical considerations and professional integrity in the workplace.	5
2	They develop their knowledge and understanding of banking and finance including concepts, theories, and analytical tools that serve both in national and international markets.	4
3	They gain an understanding of the role of financial management in business firms and the essentials of corporate finance and further develop their knowledge in the field.	4
4	They are able to apply valuation models to estimate the price of different financial assets, measure risk and describe the risk-return tradeoff.	4
5	They are provided with the knowledge and understanding of the regulatory framework and functioning of banking system and central banking as well as international banking system.	2
6	They are able to understand and use fundamental economic theories and tools to solve economic problems in banking and financial services industry.	3
7	They have the ability to develop and utilize accounting, financial and economic data as well as other information to solve different business problems by making use of basic mathematical and statistical models.	5
8	They are expected to develop their numerical and IT skills as well as knowledge of databases in order to address the significant development in the delivery and use of financial services known as FinTech.	4
9	They develop their ability to think critically, do research, analyze, interpret, draw independent conclusions, and communicate effectively, both individually and as part of a team.	4
10	They are provided with opportunities to acquire the necessary skills and competencies to develop professionalism in the banking and financial services industry or to move on to further study within the discipline.	4

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	30
Presentation	1	20
Final Exam	1	40
Attendance		10
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	2	32
Mid-terms	1	1	1
Assignments	1	10	10
Final examination	1	14	14
Other	9	5	45
Total Work Load:			150
Total Work Load/25(h):			6
ECTS Credit of the Course:			6