543ECON: Financial Econometrics

 
543ECON: Financial Econometrics
Lecture 1

 

Course Description
Course: Economics 543, Financial Econometrics
2nd term 1435/1436; 3 units
Professor: A-M. M. Abdel-Rahman

Lecture Room: L6
Office.
Office Phone: 4674155; 0504180249
Email: abdelma@ksu.edu.sa; ammar04@gmail.com
Required Text: Chris Brooks, Introductory Econometrics for Finance, 2nd edition (Cambridge University Press), 2008.
The textbook could be ordered through Amazon Publishers.
Examinations: There will be two midterm tests and a final examination.
Homework: Problem sets may be assigned.
Grading: Usually each of the two midterms is assigned 25% of the course mark, the homework assignments count as 10%, and the final is 40%. 

  • First Test:                   Week 6
  • Second test:                Week 10
  • Final:                          Determined by College Examinations Committee

Econometrics for Finance: We define financial econometrics as 'the application of econometric techniques to problems in finance'.
Although econometrics is often associated with analyzing economic problems such as economic growth, consumption, investment, demand, production and cost analysis the applications in the areas of finance have grown rapidly in the last few decades. This is due to the fact that financial markets generate vast amounts of data on asset returns, and other financial variables in long and high-frequency time series. The ability to analyze these data requires good knowledge of statistical and econometric methods of analysis. In addition and since the early 1980s techniques for analyzing time series, have yielded important studies of financial markets, increasing our knowledge and insight of financial variables’ behavior and volatility.
Thus the objective of this course is to extend your knowledge and equip you with econometric methods and techniques that would allow you to analyze empirically finance-related issues.
Econometric Software: For application purposes we will rely on the software package widely adopted for university econometrics courses, EViews.
Objectives and learning outcomes of the course: By the end of this course you will be able to:

  • Formulate econometric models suitable for financial analysis.
  • Apply methods of econometric modeling and analysis in the context of financial market data and evaluate their results through appropriate diagnostic testing procedures.
  • Select appropriate methods of estimation and interpret the obtained results.
  • Understand the principles of time series modeling and evaluate their ability to represent, analyze and forecast financial variables.
  • Study models for volatility and be able to apply them to financial time series which display that volatility.

Scope and syllabus:
1.     Review of the Linear Regression Model (LRM)
a.      The Simple Linear Regression Model
b.     The General Linear Regression Model (GLM)

 
2.     Problems and Testing Procedures in the Regression Models:
a.      Multicollinearity:
                                           i.     Causes
                                         ii.     Testing
                                       iii.     Consequences
                                        iv.     Solutions
b.     Heteroscedasticity
                                           i.     Causes
                                         ii.     Testing:
1.     White test
                                       iii.     Consequences
                                        iv.     Solutions
c.      Autocorrelation
                                           i.     Causes
                                         ii.     Testing:
1.     Durbin – Watson test
2.     The LM test
                                       iii.     Consequences
                                        iv.     Solutions
d.     Other testing procedures
                                           i.     Chow structural breaks
                                         ii.     …
3.     Time Series Modeling    
a.      Modeling long-run relationship in finance        
4.     Volatility and ARCH models     
 
 

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