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Program Type: 
Thesis
Non Thesis
Course Code: 
FE 622
P: 
3
Lab: 
0
Credits: 
3
ECTS: 
10
Course Language: 
English
Course Content: 

Introduction to Fourier transforms (FT): Definition, properties and continuous time FT maps. Power spectrum, Wiener-Kintchene formula, Fast Fourier Transforms (FFT). Problems with FFT.

Linear filters and volatility estimation. Wavelet and wavelet transforms (WT): Definition and properties, discrete time wavelet transforms (DWT), Haar, Daubechies, Symlet, etc. wavelets.

Multiple resolution analysis (MRA). Deflecting with wavelet transforms. Sharpening with discrete time wavelet transforms. Wavelets and long-term memory. Stein's unbiased risk estimation.

Teaching Methods: 
1: Lecture, 2: Question-Answer, 3: Discussion
Assessment Methods: 
A: Testing, B: Homework

Vertical Tabs

Course Learning Outcomes

Learning Outcomes Programme Learning Outcomes Teaching Methods Assessment Methods
Analysis of Fourier transforms (FT)   1,2,3 A,C
Linear filters and volatility estimation   1,2,3 A,C
Detection of noise with wavelets   1,2,3 A,C
Determining the trend with wavelets   1,2,3 A,C
Determination of seasonality effect with wavelets   1,2,3 A,C

Course Flow

COURSE CONTENT
Week Topics Ön Hazırlık
1 Introduction  
2 Introduction to Fourier transforms (FT)  
3 Power spectrum, Wiener-Kintchene formula  
4 Fast Fourier Transforms (FFT). Problems with FFT  
5 Linear filters and volatility estimation  
6 Wavelet and wavelet transforms (WT)  
7 Discrete time wavelet transforms (DWT), Haar wavelets  
8 Daubechies wavelets  
9 Symlet, Coiflet wavelets  
10 Multiple resolution analysis (MRA)  
11 Midterm  
12 Deflecting with wavelet transforms  
13 Sharpening with discrete time wavelet transforms  
14 Wavelets and long-term memory  
15 Stein's unbiased risk estimate  
16 Final  

Recommended Sources

RESOURCES
Course Notes R. Gençay, F. Selçuk and B. Whitcher, An introduction to wavelets and other filtering methods in finance and economy Academic Press 2002
Others Resources  

Assessment

ASSESSMENT
IN-TERM STUDIES NUMBER PERCENTAGE
Mid-Term 1 60
Assignment 10 40
Final Exam 1 100
  Total 100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL
GRADE
  50
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL
GRADE
  50
  Total 100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAMME
No Program Learning Outcomes Contribution
1 2 3 4 5  
1 It uses the knowledge and skills it has internalized in the fields of Economics, Finance, Statistics and Computer Science in interdisciplinary studies and produces different fields of application.       X    
2 With the awareness of lifelong learning and questioning, it follows national and international publications; It is expected to expand the limit of knowledge with scientific articles by reaching the level of preparing works in accordance with academic rules.       X    
3 Designs, implements, solves and interprets analytical, modeling and empirical research; This way it makes predictions.     X      
4 When she is involved in business life, she is expected to blend her knowledge in different fields with her differences and competencies and reflect them to her individual career.   X        
5 With his English proficiency, he follows the knowledge and developments in his field at an international level and communicates with his colleagues.       X    
6 Uses computer software and information and communication technologies required by related fields at an advanced level.         X  

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION  
 
Activities Quantity Duration (Hour) Total Workload (Hour)  
Course Duration (Including the exam week: 15x Total
course hours/week)
16 3 48  
Hours for off-the-classroom study (Pre-study, practice,
review/week)
16 5 80  
Homework 10 6 60  
Mid-term 1 20 20  
Final 1 40 40  
Total Work Load     248  
Total Work Load / 25 (h)     9,92  
ECTS Credit of the Course     10