Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Podmínky zakončení předmětu -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Literatura -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Požadavky ke zkoušce -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Sylabus -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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
Vstupní požadavky -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Požadavky k zápisu -
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
Poslední úprava: doc. PhDr. Jozef Baruník, Ph.D. (18.09.2023)
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.
