SubjectsSubjects(version: 916)
Course, academic year 2022/2023
   Login via CAS
Financial Econometrics II - JEM061
Title: Financial Econometrics II
Czech title: Financial Econometrics II
Guaranteed by: Institute of Economic Studies (23-IES)
Faculty: Faculty of Social Sciences
Actual: from 2022
Semester: winter
E-Credits: 6
Examination process: winter s.:
Hours per week, examination: winter s.:2/2, Ex [HT]
Capacity: 40 / 40 (22)
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: English
Teaching methods: full-time
Note: course can be enrolled in outside the study plan
enabled for web enrollment
priority enrollment if the course is part of the study plan
Guarantor: doc. PhDr. Jozef Baruník, Ph.D.
Mgr. Lukáš Vácha, Ph.D.
Teacher(s): doc. PhDr. Jozef Baruník, Ph.D.
Mgr. Luboš Hanus
Mgr. Lukáš Vácha, Ph.D.
Class: Courses for incoming students
Annotation -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (16.09.2022)
Course description and Objectives:
The objective of the course is to introduce advanced methods for financial data. We will cover two main topics using machine learning including neural networks, recurrent networks, distributional networks and time varying parameter methods. Students will be able to use the modern financial econometric tools after passing this course.
Course completion requirements -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (16.09.2022)

There will be two empirical projects based on the methods covered during the lectures. 

Project 1 (P1) details: Students are encouraged to form a group of two for project 1 which will be assigned in three small parts during the semester. Evaluation of this project will be during the midterm week when students will present and discuss their results.

Project 2 (P2) details: This project will be delivered by each student individually. Last week of the course we will hold a workshop on progress of the works on the project. The evaluation of projects will consist of presentation and defense of the project. Each student will present their own project and at the same time will be asked to evaluate and discuss the project of a colleague (randomly assigned). 

Grading:
Midterm exam  - P1 defense and evaluation:                  0 - 40% 
Final exam - P2 defense and evaluation:                        0 - 60% 

Grading (A-F) - in line with the Dean's decree 17/2018. 

Literature -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (16.09.2022)

Stan Hurn, Vance Martin, Peter Phillips and Jun Yu (2021): Financial Econometric Modelling

Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in Finance (Vol. 1406). New York, NY, USA: Springer International Publishing.

De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.

Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.

Klok, H., & Nazarathy, Y. (2019). Statistics with julia: Fundamentals for data science, machine learning and artificial intelligence.

Requirements to the exam -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (16.09.2022)

There will be two empirical projects based on the methods covered during the lectures. 

Project 1 (P1) details: Students are encouraged to form a group of two for project 1 which will be assigned in three small parts during the semester. Evaluation of this project will be during the midterm week when students will present and discuss their results.

Project 2 (P2) details: This project will be delivered by each student individually. Last week of the course we will hold a workshop on progress of the works on the project. The evaluation of projects will consist of presentation and defense of the project. Each student will present their own project and at the same time will be asked to evaluate and discuss the project of a colleague (randomly assigned). 

Grading:
Midterm exam  - P1 defense and evaluation:                  0 - 40% 
Final exam - P2 defense and evaluation:                        0 - 60% 

Grading (A-F) - in line with the Dean's decree 17/2018. 

Syllabus -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (16.09.2022)

1. Introduction Lecture + seminar Introduction to Julia
2. A route toward nonlinear regression
3. Neural Networks
4. Recurrent Neural Networks
5. Classification
6. Distributional forecasting
7. Midterm week - Project P1 evaluation workshops
8. Time Varying Parameter (TVP) Models intro
9. TVP Estimation: kernel and non-parametric statistics
10. TVP Estimation: localized likelihood methods
11. TVP applications in Finance
12. Workshops on Project 2 progress

Entry requirements -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (16.09.2022)

There are no formal course requirements. However, knowledge up to the level of Financial Econometrics I (JEM059), Advanced Econometrics (JEM005) courses is assumed and expected.

Registration requirements -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (16.09.2022)

There are no formal course requirements. However, knowledge up to the level of Financial Econometrics I (JEM059), Advanced Econometrics (JEM005) courses is assumed and expected.

 
Charles University | Information system of Charles University | http://www.cuni.cz/UKEN-329.html