
Show Notes
Employ put–call parity to replicate forwards, build collars, and decompose equityvalue into debt + option components. Extend to forward parity for off-marketstrikes and see how corporate finance uses these identities.
More from Deep Dive: CFA® Level I Prep 2026
Reading 7: Estimation & Inference with Mara Ellington & Dorian Hayes. In this bite-sized CFA L1 episode, we turn symbols into intuition: Sampling methods: simple random, stratified, cluster, convenience & judgmental. Central Limit Theorem: why sample means of size n behave (almost) normal. Resampling: using bootstrap & jackknife when theory won’t give you a clean formula. Quick, focused revision for your next Quant mock.
Dive into Simulation Methods from Quantitative Methods – CFA Level I with Mara Ellington & Dorian Hayes. Normal vs lognormal curves for asset prices & returns How Monte Carlo paths price risk in portfolios Using bootstrap resampling when data is limited Turn randomness into insight, one simulation at a time.
Portfolio Mathematics – CFA Level IJoin Mara Ellington and Dorian Hayes as they turnChapter 1 Quantitative Methods into something you can actually use. Build intuition for expected return, variance & risk. See how covariance and correlation shape portfolios. Use Roy’s safety-first ratio to guard against shortfall risk. Perfect bite-sized review for your CFA Level I prep.
CFA® Level I Boost w/ Mara Ellington & Dorian Hayes Learn how to turn messy scenarios into clean probability trees and sharp conditional expectations—in one focused session. Draw & read probability trees like an analyst Master P(A|B) = P(A∩B)/P(B) without fear Apply the logic to payoffs, defaults & simple return paths Perfect for commute-length revision and exam-day clarity.
Statistical Measures of Returns with Mara Ellington & Dorian Hayes. In this bite-size Quant Methods episode, we turn raw return data into insight: Means that matter: arithmetic vs. geometric returns (μ, g). How variance, standard deviation & downside risk frame volatility (σ, σ2). Reading the shape: skewness, kurtosis & (non-)normality. Why cov(Ri, Rj) and ρ drive diversification. Perfect if you want CFA Level I stats to finally “click”.