Objectives and competences
Key learning objectives:
Students enhance and build on the theoretical knowledge of quantitative risk management and understand the processes of making business decisions under uncertainty. They also acquire the ability to use tools and analytical instruments to identify, measure, optimise and predict risks, including an understanding of real options. In addition, they acquire an in-depth knowledge of user interface methods and their application in finance and risk management.
Content (Syllabus outline)
1. Fundamentals in quantitative risk management (Introduction to Risk Analysis, Monte Carlo Simulation with Risk Simulator, Advanced Simulation Techniques)
2. Analytical tools, optimization and forecasting in Risk Simulator Software
3. Real Options and Industry Applications
4. Real Options Analysis: Theory and Background, Real Options Analysis: SLS (Super Lattice Solver) Application
5. AI in finance (Basic concepts of AI, machine learning and neural networks, application of AI methods in Risk Simulator and Matlab).
Learning and teaching methods
- common lectures;
- AV presentations;
- case studies and industry applications from multinationals companies;
- hands-on modelling and exercises;
- modelling with use of computer;
- team work with active participation.
Intended learning outcomes - knowledge and understanding
Students in this course:
1. Develop the ability to identify, assess and quantify, evaluate, model, optimize and predict risks and uncertainties in economic and non-economic quantities (PIL 2a, PIL 2b).
2. Develop the ability to carry out Monte Carlo risk simulations, econometric and predictive modelling, perform risk diversification in the portfolio ( PILO3a, PILO 3b).
3. Critically evaluate the implemented risk simulations, analyst and forecasts (PILO 3a, PILO 3b).
4. Understand the background, methods and application of advanced methods in risk management and finance, and are able to choose the appropriate method to obtain a solution to a problem (PILO 2b, PILO 3a).
5. Are able to interpret and analyze business data and how to make business decisions based on the acquired risk analysts (PILO 3a, PILO 3b).
6. Are able to use advanced AI models for risk modelling (PILO2b, PILO 3a).
7. Are aware of their own ethical and professional responsibility in the field of data science (PILO 4a).
8. Critically evaluate the sustainable and social impact of using advanced methods and AI in business decision-making (PILO 4b).
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Intended learning outcomes - transferable/key skills and other attributes
Students in this course:
1. Develop the ability to identify, assess and quantify, evaluate, model, optimize and predict risks and uncertainties in economic and non-economic quantities (PIL 2a, PIL 2b).
2. Develop the ability to carry out Monte Carlo risk simulations, econometric and predictive modelling, perform risk diversification in the portfolio ( PILO3a, PILO 3b).
3. Critically evaluate the implemented risk simulations, analyst and forecasts (PILO 3a, PILO 3b).
4. Understand the background, methods and application of advanced methods in risk management and finance, and are able to choose the appropriate method to obtain a solution to a problem (PILO 2b, PILO 3a).
5. Are able to interpret and analyze business data and how to make business decisions based on the acquired risk analysts (PILO 3a, PILO 3b).
6. Are able to use advanced AI models for risk modelling (PILO2b, PILO 3a).
7. Are aware of their own ethical and professional responsibility in the field of data science (PILO 4a).
8. Critically evaluate the sustainable and social impact of using advanced methods and AI in business decision-making (PILO 4b).
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Readings
Johnathan Mun, (2015), »Modeling Risk: Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, Portfolio Optimization, Data Analytics, Business Intelligence, and Decision Modeling«, Hardcover – 1 Wiley; 3rd ed. edition (1 Aug. 2015),Language ? : ? English, ISBN-10 ? : ? 1943290008, ISBN-13 ? : ? 978-1943290000. Pridobljeno 26. aprila 2023: https://www.researchgate.net/publication/338035828_Modeling_Risk_Applying_Monte_Carlo_Risk_Simulation_Strategic_Real_Options_Analysis_Stochastic_Forecasting_and_Portfolio_Optimization_3rd_Edition.
Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jesus, Neural Network Design (2nd Edition), ISBN-10: 0-9717321-1-6, ISBN-13: 978-0-9717321-1-7. pridobljeno 26. aprila 2023: https://hagan.okstate.edu/NNDesign.pdf.
Additional information on implementation and assessment - Written exam (100%)
Written exam - individual
written exam
