Explore every episode of the podcast 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.

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.

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”.

Time Value of Money Deep Dive with Mara Ellington & Dorian Hayes.

In this episode we turn abstract formulas into real decisions:

• Present value (PV) of bonds & equities from future cash flows
• Implied returns from today’s market prices
• Cash-flow additivity & the no-arbitrage mindset
• Forward rates, FX forwards & a first look at options

Perfect for CFA Level I candidates reviewing Quant Methods, Chapter 1.

Hosted by Mara Ellington & Dorian Hayes

Ready to finally “get” rates & returns instead of memorising formulas? In this bite-size episode we turn Quant Methods – Chapter 1 into clear, exam-ready intuition.

• Holding period, arithmetic & geometric mean returns – when each one actually matters.
• Nominal vs real return (and why inflation quietly eats your gains).
• Simple, compound & effective annual rates: from r to EAR step-by-step.
• Cash flows on a timeline: discounting & compounding made visual.

Goal: walk away able to explain every key formula without looking at your notes.

This module brings ethics to life through realistic scenarios and case studies that apply the Code and Standards. You'll practice identifying violations, applying ethical reasoning, and making judgment calls in complex situations. It’s all about turning ethical principles into confident, day-to-day decision-making.

This module introduces the Global Investment Performance Standards (GIPS), a set of voluntary, ethics-based principles that promote fair and transparent reporting of investment results. You'll learn why GIPS exist, who uses them, and how they help clients compare performance across firms by standardizing calculations, disclosures, and presentation practices.

Standard VII reinforces the duty to uphold the reputation and integrity of the CFA designation. It requires members and candidates to comply with the Code and Standards, avoid any conduct that could harm the CFA Institute, and never misrepresent the meaning or implications of earning or pursuing the CFA charter.

Standard VI addresses the importance of identifying and managing conflicts of interest that could compromise objectivity. It requires full and fair disclosure of any potential conflicts—especially those involving ownership, compensation, or referral arrangements—so that clients and employers can make informed decisions with confidence.

Standard V focuses on maintaining the integrity and quality of investment research and decisions. It requires professionals to base analyses on thorough due diligence, clearly communicate investment recommendations and risks, and retain records that support the investment process. The standard ensures transparency and accountability in all investment actions.

Standard IV outlines the responsibilities investment professionals have toward their employers. It covers the duty of loyalty, maintaining competence, and acting with integrity while on the job. It also addresses proper conduct when leaving a firm and the importance of not misusing employer resources or confidential information.

Standard III emphasizes the fiduciary responsibility investment professionals owe to their clients. It outlines duties such as loyalty, prudence, and care; ensuring fair dealing; preserving confidentiality; and providing suitable, objective advice. This standard reinforces that clients’ interests must always come first.

Standard II protects the fairness and transparency of global capital markets. It focuses on two key areas: Material Nonpublic Information—ensuring analysts don’t misuse inside information—and Market Manipulation—prohibiting practices that distort prices or trading volume. The goal: promote investor confidence by keeping markets level and trustworthy.

Standard I covers the fundamental duty of acting with integrity and professionalism. It sets expectations around knowledge of the law, independence and objectivity, misrepresentation, and misconduct. You'll learn how to navigate conflicts, avoid biased research, and maintain trust in your professional interactions—especially when legal and ethical standards diverge.

This module introduces the CFA Institute Code of Ethics and the Standards of Professional Conduct, the backbone of ethical investment practice. You'll explore the six core ethical responsibilities and seven standards that guide duties to clients, employers, markets, and the profession. Each standard is explained with practical examples to help you apply them in real-world scenarios.

This opening module explains why ethics and trust are foundational to functioning capital markets. It explores how ethical conduct enhances investor confidence, defines professionalism, and reduces systemic risk. You'll also learn to distinguish ethical standards from legal ones, and understand the consequences when fiduciary duty is breached. This sets the tone for the rest of the Ethics curriculum.

The final chapter frames risk management as an enterprise-wide process. It describes risk governance, risk tolerance and budgeting, distinguishes financial from non-financial risks, and evaluates tools for measuring, preventing, accepting, transferring, or shifting risk—providing a comprehensive toolkit for modern risk managers. :contentReference[oaicite:5]{index=5}

Here the curriculum turns to behavioral finance, categorizing cognitive errors and emotional biases that can distort investor decisions. It illustrates common biases (e.g., overconfidence, loss aversion), links them to market anomalies such as momentum and bubbles, and discusses how awareness of these tendencies can improve wealth-management advice. :contentReference[oaicite:4]{index=4}

Focusing on practical implementation, this chapter explains the Investment Policy Statement—its key components, objectives, and constraints—and shows how client information feeds into capital-market expectations, strategic asset allocation, and ESG considerations when assembling portfolios. :contentReference[oaicite:3]{index=3}

The chapter adopts a portfolio perspective, highlighting diversification benefits and the risk–return trade-off. It walks through the three-step portfolio-management process (planning, execution, feedback), profiles individual and institutional investors, and surveys the structure and current trends of the global asset-management industry. :contentReference[oaicite:2]{index=2}

Building on Part I, this chapter introduces capital-market theory. It derives the Capital Market Line and the Security Market Line, decomposes total risk into systematic and nonsystematic components, develops the CAPM and beta, and presents key risk-adjusted performance measures such as the Sharpe, Treynor, M² and Jensen’s alpha. :contentReference[oaicite:1]{index=1}

This chapter lays the foundations of modern portfolio theory. It reviews the historical risk-return profile of major asset classes, explains risk aversion and utility, shows how diversification shapes the minimum-variance and efficient frontiers, and demonstrates how investors combine a risk-free asset with risky portfolios to reach an optimal allocation. :contentReference[oaicite:0]{index=0}

Long/short, event, relative-value, macro … Unpack the strategy zoo.

• Key features: leverage, shorting, derivatives and flexible mandates.
• Direct vs. fund-of-funds vs. liquid-alt wrappers.
• Risk/return drivers, common draw-downs and true diversification worth.

You’ll finish able to decode any hedge-fund pitch-book in under five minutes.

Timber, farmland, and commodities join the alternatives toolkit.

• Contrast land-based investments (farmland, timberland) with commodity futures rolls.
• Dive into supply-and-demand drivers, storage costs and seasonality.
• Explore inflation-hedging properties and where natural resources fit alongside real estate in the real-asset bucket.

Perfect if you’ve ever wondered why soybeans and gold dance to verydifferent macro tunes.

Bricks, mortar & bridges. Compare the cash-flow engines ofincome-producing property and long-lived infrastructure projects.

• Equity vs. debt vs. REIT structures for property exposure.
• Greenfield, brownfield and secondary-stage infrastructure—how risk and return step down each stage.
• Inflation linkage, GDP sensitivity and diversification traits of each sub-asset.

After this module you’ll know why toll roads trade like bonds and officesbehave like equities.

From start-ups to leveraged buy-outs. Map the spectrum ofprivate equity (VC, growth, buy-out) and private debt (direct lending,mezzanine, distressed).

• Key terms: committed vs. called capital, exit routes, GP/LP economics.
• Risk–return expectations and why valuation lags can mask volatility.
• Diversification benefits and where private capital slots into a broader portfolio.

Essential listening before you quote IRRs at networking drinks.

Measuring performance is trickier here. Longer life-cycles,irregular cash flows, leverage and complex fees mean IRR and MOIC trump simpletime-weighted returns.

• See capital commitment → deployment → distribution phases and the infamous J-curve.
• Work through fee waterfalls: management, incentive, hurdles, high-water marks, clawbacks.
• Calculate before- and after-fee returns and spot survivorship bias when benchmarking.

You’ll learn why “headline” numbers can mislead—and how to adjust them.

Why alternatives? They promise higher expected returns andlower correlations—but at the cost of liquidity, longer holding periods andspecialised know-how. This module:

• Distils the three big categories — private capital, real assets and hedge funds.
• Compares fund, co-investment and direct routes, showing how control, fees and effort shift across methods.
• Explains ownership & compensation structures (limited partnerships, trusts, LLCs) and why performance-based fees align managers and investors.

The perfect launch-pad for everything that follows.

Construct a simple up/down binomial tree to price a European call. Learn risk-neutralprobabilities, replicate pay-offs with ∆-hedged portfolios, and generalise theframework for any derivative where pay-off depends on one period of pricemovement.

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.

Break option value into intrinsic and time components, test moneyness, andstudy six core inputs (spot, strike, time, rate, volatility, income). Use arbitrage andreplication arguments to bound fair prices before jumping into models later.

View a plain-vanilla swap as a strip of forward contracts: solve for the par swaprate that sets initial value to zero, then track mark-to-market gains as rates move.Covers fixed-for-float interest swaps, currency swaps and the impact of clearing.

Derive fair-value futures prices at inception, then follow daily variation margin tosee how gains/losses crystallise. Compare futures with equivalent forwards, explorerate-futures versus FRAs, and note how central clearing reshapes counter-partyrisk.

Price and mark-to-market forwards between trade date and expiry—first fornon-income assets, then for instruments with cash flows. Extend the logic tounderlyings that have their own term structures (rates, FX), and master forwardrate agreements (FRAs) as a special case.

Link spot and forward prices through no-arbitrage. Build synthetic forwards bycombining cash positions with borrowing/lending, then adjust for dividends,storage, or FX rate differentials in the cost-of-carry. Understand why forwardprices can trade above or below spot—depending on net carry.

See how issuers hedge earnings, how investors gain cheap leverage or preciseexposures, and where dangers lurk: basis, liquidity, counter-party and systemicrisk. Real examples show futures lowering funding needs and swaps smoothingcash-flow volatility—alongside cautionary tales of excessive leverage.

Firm promises vs. flexible rights: dissect forwards, futures and swaps(linear pay-offs) then contrast them with options and credit derivatives(non-linear, buyer-only choice). Learn how margin, daily settlement and embeddedleverage shape risk, and why put–call parity lets you build a synthetic forward fromtwo options.

Quick tour of the derivatives universe: define what makes a contract aderivative, classify popular underlyings (equities, rates, FX, commodities, credit),and compare OTC versus exchange-traded markets—including how centralcounterparties (CCPs) reduce default risk. By the end you’ll know where eachinstrument trades, why standardisation matters for liquidity, and how margin keepsthe system safe.

From pass-throughs to CMOs we unpack prepayment mechanics, tranche types and commercial vs. residential differences. Understand PSA curves, extension risk and how agencies wrap credit.

Explore structural credit enhancement, tranche waterfalls and trigger tests. Compare classic asset classes—autos, cards—and newer niches like solar leases and whole-business deals.

Follow the pipeline from loan pool to special-purpose entity to tranche. See how credit- and time-tranching reshape risk for issuers and investors, plus the economic case for securitization.

Dive into business risk, moats, management and detailed financial ratios. Finish with recovery analysis across the capital stack and why ratings can lag the market.

Blend qualitative governance checks with quantitative debt, growth and external-balance ratios to rank sovereign default risk. Contrast with sub-sovereign and supranational analysis.

Define default, recovery and credit migration. Walk through structural vs. reduced-form models and see how ratings, leverage and cyclical forces feed into spread moves.

Swap from yield-shift to curve-shift analytics: key-rate durations, twist & butterfly exposures, plus empirical (regression) duration when cash-flow data are messy.

Add curvature to your risk toolkit. Topics: standalone bond convexity, duration-plus-convexity price estimates and building target-duration/convexity portfolios.

Compute modified, money duration and price-value-of-a-basis-point for bullets, zeros, floaters and perps. See how convexity tweaks the linear estimate.

Break total return into income, reinvestment and price change. Link Macaulay duration, investment horizon and curve shifts to work out when rising rates can still boost realised return.