Simulation Methods – Module 6 – Quantitative Methods – CFA® Level I 2026
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00:00 Introduction: Log Normal Distributions, Monte Carlo & Bootstrapping
Overview of key concepts for CFA Level 1
Why these tools matter for dealing with market uncertainty
00:24 Log Normal Distributions Explained
Difference from normal distributions (, positively skewed)
Application to stock prices and continuously compounded returns
01:39 Continuously Compounded Returns & Stock Prices
Relationship between future stock price and log normal distribution
Holding period return vs. continuously compounded return
02:56 IID Assumptions & Volatility Measures
Independently and identically distributed returns
Annualized volatility formula
High vs. low volatility stocks in risk assessment
05:08 Monte Carlo Simulation: Steps & Applications
Specify underlying variables (e.g., stock price)
Define time period & assumption about risk factors
Distributional assumptions (random draws from known distributions)
Estimate underlying variables & option values
Discount to present value
Repeat thousands of times to derive mean estimate
Use cases: pricing complex securities (options, MBS), risk analysis
08:00 Bootstrapping & Resampling
Difference between bootstrapping and other sampling methods
Drawing new samples “with replacement” from existing data
Estimating parameters (mean, variance) directly from empirical data
09:30 Monte Carlo vs. Bootstrapping
Monte Carlo Simulation: generates new data from assumed distributions
Bootstrapping: resamples existing empirical data
When to use each approach in finance
12:02 Conclusion & CFA Exam Tips
Recap of key terms: log normal distribution, continuous compounding, Monte Carlo, bootstrapping
Importance for risk management and investment modeling
Final reminder to practice and review for CFA Level 1