Monte Carlo simulation is a technique for predicting outcomes involving uncertain events.
Analysts can use these methods to quantify risks with statistics like value at risk and tail value at
risk. In addition, actuaries can estimate the likelihood of certain events, such as the probability
of ruin or the probability of turning a profit.
In this educational project, students will learn to design Monte Carlo simulations, evaluate their
strengths and limitations, and apply them to real-world actuarial problems.
- Introduction to Monte Carlo Methods
- Study examples of Monte Carlo simulations, such as
- Estimating constants like e and
- Monte Carlo integration
- Random Walks
- Review important mathematical and actuarial models, including
- (Geometric) Brownian Motion
- Aggregate Loss Distributions
- Binomial Option Pricing
- Study examples of Monte Carlo simulations, such as
- Model Building and Simulation Experiments
- Build simulations in Python and upload them to GitHub.
- Apply sensitivity analysis by altering assumptions.
- Gauge model risk
- Compare simulation statistics to analytic results when possible.
- Investigate regulatory reserve requirements.
- Basel Accords
- Solvency II
- Results Interpretation and Reporting
- Summarize findings and evaluate the efficacy of the simulations.
- Discuss implications for actuarial risk management.
- Present findings at the end of the semester to other IRisk groups.
This project does not aim to produce published research; instead, it will give a hands-on, practical introduction to Monte Carlo simulation methods. We will have weekly in-person team meetings at my office, 272 CAB, to discuss progress. There will also be individual meetings as needed to discuss members’ contributions. Students must commit at least five hours a week to the project, including meetings.
Supervisor: Eric Icaza