Simulation Techniques in Risk Management
At the end of this course the student will have acquired the knowledge, skills and competences that will allow them to:
Learn introductory concepts related to simulation;
Understand the difference between discrete-time and continuous-time simulation;
Understand what PRN pseudo random number generation methods can be used, as well as the advantages and disadvantages of each;
Use either the Excel Visual Basic module or the R Cran to solve case studies;
Build simulation modules to plan experiments and perform their statistical analysis of results;
Solve multiple simulation case studies in risk management.
This course will also provide the foundations to develop expertise in the areas of Actuarial Studies and Finance.
Nelson Fernando Chibeles Pereira Martins
Weekly - 4
Total - 84
Elementar Statistics, Programming.
Law, A. M. e Kelton, W. D., Simulation modeling and Analysis, McGraw-Hill International Editions, 2007.
Kelton, W.D., Sadowski, R.P. e Swets, N.B., Simulation with ARENA (5ª ed.), McGraw-Hill International Editions, 2009.
Banks, J. et al., Handbook of Simulation, John Wiley & Sons, 1998.
Korn, R., Korn, E., Kroisandt, G. Monte Carlo Methods and Models in Finance and Insurance CRC Press,CRC Financial Mathematics Series. 2010
Banks, J. et al., Discrete-Event System Simulation (3ª ed.), Prentice-Hall, 2001.
Klugman, Panjer and Willmot Loss Models: From Data to Decisions 4th Edition, John Wiley & Sons. 2012.
Chung, C.A., Simulation Modeling Handbook. A Practical Approach, CRC Press, 2004.
Pidd, M., Computer Simulation in Management Science, John Wiley & Sons, 1994.
The teacher presents the themes using slides and privileging the exchange of ideas to reach the goal of each lesson. Students perform practical application of the concepts acquired in class throughout the semester. Whenever possible the matter is illustrated with real examples from insurance and Social Security. Classes take place in the laboratory to be possible to access content on the internet and solving using Excel.
Attendance at the course is mandatory.
2 Tests (50%) + 1 Group Assignment (30%) + Case Studies solved in class (20%)
1 - Introduction
Terminology and basic concepts
Simulation in Discrete Time vs. Continuous Time
2 - PRN Generation Methods
Desirable qualities in the PRN: randomness tests; assessment of the independence between consecutive PRN
The inversion method
The rejection method
Monte Carlo Simulation
3 - Using the Excel Visual Basic module or R Cran for simulation.
4 - Planning of experiments and statistical analysis of results:
Number of simulations;
Stop Criterion Calibration of the model Statistical adjustment tests
Validation of results
5 - Applications in risk management
Programs where the course is taught: