Analytics of Finance

Data is displayed for academic year: 2023./2024.

Lecturers

Prof.
Zvonko Kostanjčar

Course Description

This course covers the main quantitative methods of finance. It covers three broad sets of topics: financial econometrics, statistical machine learning for financial applications and dynamic programing. This course gives a comprehensive introduction to financial economics, and particularly asset pricing. Provides the foundation for the main analytical techniques and quantitative methods necessary in the financial industry Examples of applications include portfolio optimization, risk management, and proprietary trading.

Study Programmes

University graduate
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Learning Outcomes

  1. Define main notions in the quantitative finance
  2. Explain mathematical backgrounds of main quantitative methods in finance
  3. Define and differentiate the types of financial data
  4. Differentiate between different asset pricing models
  5. Explain the basics of portfolio theory and apply portfolio optimization methods
  6. Justify the adequacy of statistical methods and machine learning algorithms for applications in quantitative finance

Forms of Teaching

Lectures

Lectures will take place for 3 hours a week.

Laboratory

Laboratory exercises are organized as projects.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 25 % 40 % 25 % 40 %
Mid Term Exam: Written 0 % 30 % 0 %
Final Exam: Written 0 % 30 %
Exam: Written 50 % 60 %
Comment:

Week by Week Schedule

  1. Classification in computational finance
  2. Modelling in computational finance
  3. Normal distribution; Lognormal distribution, Models in continuous time; Introduction to diffusion porocesses; Wiener process
  4. Concept of risk; Risk and expected return on a portfolio ; Portfolio optimization; , Markowitz portfolio theory; Efficient portfolio; Calculating the efficient frontier
  5. Capital asset pricing model (CAPM); Testing the Capital Asset Pricing Model: An Econometric Approach; Beta factor, Capital market line; Security market line
  6. Principle of optimality, Bellman equation
  7. Numerical approximation (discretizing dynamics)
  8. Midterm exam
  9. Numerical methods for portfolio optimization
  10. Generating random numbers
  11. Variance reduction
  12. Quasi-Monte Carlo method
  13. Pairs trading, Contrarian methods
  14. Momentum-based strategies, Cointegration-based trading
  15. Final exam

Literature

(.), Statistics and Data Analysis for Financial Engineering, David Ruppert, Springer, 2011,
(.), Advances in Financial Machine Learning, De Prado, Marcos Lopez, Wiley; 1 edition, 2018,
(.), The Econometrics of Financial Markets, Campbell, John Y., Andrew W. Lo, and A. Craig MacKinlay, Princeton, NJ: Princeton University Press, 1996.,
(.), Asset Pricing, Cochrane, John H, Revised ed. Princeton, NJ: Princeton University Press, 2005.,

For students

General

ID 222444
  Winter semester
5 ECTS
L1 English Level
L1 e-Learning
45 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

89 Excellent
76 Very Good
63 Good
50 Sufficient

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