Corporate Risk Management

  • Credits: 5
  • Ending: Examination
  • Range: 2P + 2C
  • Semester: summer
  • Year: 1
  • Faculty of Business Economics with seat in Košice

Teachers

Included in study programs

Corporate Business and Marketing

Teaching results

The aim of the course is to provide comprehensive concepts of risk management in companies for students. The course provides an overview and interpretation of tools to understand and master the theoretical and practical knowledge related to the identification, analysis, measurement, and assessment of risk. The course also focuses on use of new methods and tools of risk management for selection of appropriate strategies.
Knowledge:
The graduate will gain knowledge about corporate risk management. The knowledge covers development of risk management concepts, methods of risk identification and analysis, as well as models for risk measurement and risk assessment. Case studies analyses helps graduate to understand the basic strategies of risk management.
Skill:
The graduate will be able to create economic models using spreadsheets and apply mathematical and statistical tools to business processes. Skillset also encompasses mastering the probability theory and uncertainty estimation. Other abilities include identification, measurement, and assessment and management of corporate risks.
Competence:
The graduate will master the rules and procedures of creating economic models with elements of uncertainty and risk. They will master the process of design and assessment of economic models and simulations. The graduate will be able to provide recommendation for the transfer, reduction and elimination of risks and other economic problems.

Indicative content

Lectures:
1. Introduction to probability theory
2. Basic concepts of risk
3. Risk classification
4. Identification of risks in the company
5. Risk analysis
6. Risk assessment
7. Selection of risk variants
8. Risk management
9. Tree diagrams
10. Expert methods of risk assessment
11. Monte Carlo simulation
12. Methods of risk treatment
13. Other methods of risk reduction
Seminars:
1. Introduction to probability theory. Classical definition of probability.
2. Probability of Multiple Random Variables. Joint and union probability, probability of dependent and independent phenomena.
3. Conditional probability, Bayes' theorem.
4. Discrete random variable. Discrete distributions and their application in risk analysis.
5. Continuous random variable. Continuous distributions and their application in risk analysis.
6. Midterm exams I.
7. Use of MS Excel for economic analysis.
8. Creation of models using MS Excel.
9. Sensitivity analysis using MS Excel.
10. Scenario planning using MS Excel.
11. Monte Carlo simulation. Monte Carlo simulation procedure. Monte Carlo simulation in MS Excel.
12. Rules of decision-making in conditions of risk. Case Study I: Risk assessments using descriptive characteristics: percentiles, median, mode, skewness, sharpness.
13. Midterm exam II.

Support literature

Elementary literature:
1. LEHMAN, Dale; GROENENDAAL, Huybert. Practical Spreadsheet Modeling Using@ Risk.
CRC Press, 2019.
2. YOE, Charles. Principles of risk analysis: decision making under uncertainty (2 ed). CRC
press, 2019.
3. POWELL, Stephen G.; BAKER, Kenneth R. Management science: The art of modeling with
spreadsheets. Wiley, 2009.
Supplementary literature:
1. LEHMAN, Dale; GROENENDAAL, Huybert; NOLDER, Greg. Practical spreadsheet risk
modeling for management. CRC Press, 2011.
2. HOLDEN, Craig W. Spreadsheet modeling in corporate finance. Prentice Hall, 2002.

Syllabus

Lectures: 1. Introduction to probability theory 2. Basic concepts of risk 3. Risk classification 4. Identification of risks in the company 5. Risk analysis 6. Risk assessment 7. Selection of risk variants 8. Risk management 9. Tree diagrams 10. Expert methods of risk assessment 11. Monte Carlo simulation 12. Methods of risk treatment 13. Other methods of risk reduction Seminars: 1. Introduction to probability theory. Classical definition of probability. 2. Probability of Multiple Random Variables. Joint and union probability, probability of dependent and independent phenomena. 3. Conditional probability, Bayes' theorem. 4. Discrete random variable. Discrete distributions and their application in risk analysis. 5. Continuous random variable. Continuous distributions and their application in risk analysis. 6. Midterm exams I. 7. Use of MS Excel for economic analysis. 8. Creation of models using MS Excel. 9. Sensitivity analysis using MS Excel. 10. Scenario planning using MS Excel. 11. Monte Carlo simulation. Monte Carlo simulation procedure. Monte Carlo simulation in MS Excel. 12. Rules of decision-making in conditions of risk. Case Study I: Risk assessments using descriptive characteristics: percentiles, median, mode, skewness, sharpness. 13. Midterm exam II.

Requirements to complete the course

Midterm written exams
Final written exam
Midterm evaluation:
• midterm written exams - 40% of course (total points 40)
Minimal points required to pass midterm written exams are 21 points (out of 40 points).
Final written exam:
• final written exam - 60% of course (total points 60)
Minimal points required to pass final written exams are 31 points.

Student workload

• 26 hours of participation in lectures – 26 hours
• 13 hours preparation for lectures – 13 hours
• 26 hours of participation in seminars – 26 hours
• 13 hours preparation for seminars – 13 hours
• 26 hours preparation for midterm written exams – 26 hours
• 26 hours preparation for the final exam – 26 hours
Total: 130 hours

Language whose command is required to complete the course

English

Date of approval: 20.02.2023

Date of the latest change: 14.12.2022