Objectives and competences
Key learning objectives:
1. Consolidate and upgrade theoretical knowledge in quantitative risk management
2. Understand how companies make strategic business decisions under uncertainty.
3. Are able to use tools and analytical instruments for risk identification, quantification, optimization and forecasting.
4. Understand Real Options
5. Understand AI methods and are able to use them 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
Knowledge and understanding:
Students:
- Understand the concepts and applications of quantitative risk management.
- Understand how to identify, assess, quantify, value, model, optimise, and forecast risks and uncertainties of economic and noneconomic outcomes.
- Develop the ability how to use Monte Carlo risk simulations, perform econometric and predictive modelling, and implement risk diversification in portfolios (products, assets, commodities, etc.).
Intended learning outcomes - transferable/key skills and other attributes
Cognitive/Intellectual skills:
- Critically evaluate performed risk simulations, analytics and forecasts.
- Understand background, methods and use of advanced methods in risk management and finance.
- Are able to choose the appropriate method for obtaining a solution to the problem.
Key/Transferable skills
- Are capable and know of how to run advanced analytics, to interpret and analyse business data and to make business decisions based on obtained risk analytics.
- Are able to apply advanced AI models for modelling risk.
- Are able to mitigate, hedge, and reduce risks through the applications of strategic real options techniques and models.
Practical skills:
- Use Simulation and Analytical Tools, Tools for Optimisation and Forecasting.
- Use software like Matlab, and Risk Simulator.
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 Jes?s, 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 Type (examination, oral, coursework, project):
- written examination
