QUANT - Portfolio Mathematics [2026]

QUANT - Estimation and Inference

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.

QUANT - Simulation Methods

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.

QUANT - Portfolio Mathematics [2026]

Portfolio Mathematics – CFA Level I Join 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.

QUANT - Probability Trees and Conditional Expectations [2026]

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.

QUANT - Statistical Measures of Asset Returns [2026]

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”.