FRM Part 1 Foundations Course

The FRM Part 1 exam is a computer-based test made up of 100 multiple-choice questions that candidates must complete in four hours. It covers four topics: Foundations of Risk Management, Quantitative Analysis, Financial Markets and Products, and Valuation and Risk Models, with roughly 20%, 20%, 30%, and 30% of the exam weight respectively.

Foundations of Risk Management introduces the core ideas that shape modern risk practice. Enterprise risk management, or ERM, treats risk across the whole organization as a portfolio, so that separate business units do not manage risk in isolation. The risk appetite statement translates strategy into measurable limits that the firm is willing to accept.

Strong risk governance assigns clear responsibility. The board approves risk strategy and appetite, oversees the framework, and is ultimately accountable to shareholders. Senior management implements policy, while the chief risk officer, or CRO, leads the independent risk function and reports risk exposures to the board and regulators.

Basel capital standards provide the global framework. Basel II introduced the three pillars: minimum capital, supervisory review, and market discipline; Basel III strengthened capital quality, liquidity, and leverage requirements after the 2008 crisis.

• Know the four FRM Part 1 topics and their approximate weights.
• Distinguish ERM from siloed risk management.
• Explain how risk appetite and limits guide day-to-day decisions.
• Compare board, senior management, and CRO responsibilities.
• Summarize the evolution from Basel I to Basel III.

Weight plan: markets and valuation are 30% each, quant and foundations 20% each. Study by weight, take a diagnostic, and review ethics and governance early.

Quantitative Analysis gives risk managers the tools to measure uncertainty. Probability concepts begin with random variables, expected value, variance, and covariance, which describe how outcomes are distributed and how two variables move together.

Common distributions matter in practice: the normal distribution supports many risk models, the lognormal distribution keeps asset prices positive, and the binomial distribution models discrete outcomes. Linear regression estimates the relationship between a dependent variable and one or more independent variables, while correlation and covariance summarize dependence.

Hypothesis testing lets analysts decide whether evidence supports a claim, using a null hypothesis, a test statistic, and a significance level. Monte Carlo simulation generates many random scenarios from assumed distributions and aggregates the results to estimate probabilities that are difficult to solve analytically.

Value at Risk, or VaR, summarizes the worst expected loss over a target horizon at a given confidence level. Parametric VaR assumes normality and uses the mean and standard deviation, while historical and Monte Carlo approaches use observed or simulated data.

• Compute expected value, variance, and standard deviation.
• Recognize normal, lognormal, and binomial distributions.
• Interpret regression coefficients, R-squared, and correlation.
• Apply hypothesis testing steps: null, statistic, p-value, decision.
• Explain VaR intuition and the difference between parametric, historical, and Monte Carlo VaR.

Distribution drill: know normal, lognormal, binomial, and Poisson distributions, plus expected value, variance, covariance, and correlation. Practice standardizing values and reading z-tables.

Financial Markets and Products introduces the instruments traded across bond, equity, derivative, and foreign exchange markets. Bonds are debt instruments with coupon payments and a principal repayment, equities represent ownership claims, and derivatives such as forwards, futures, options, and swaps derive their value from an underlying asset or rate.

Valuation starts with bond pricing: the price equals the present value of future cash flows discounted at the market yield. Duration measures the approximate percentage change in price for a small change in yield, and convexity captures the curvature that makes the estimate more accurate for larger moves.

Option pricing builds on payoff logic. A call gives the right to buy and a put gives the right to sell, each at a fixed strike price. The binomial model values an option by constructing a tree of possible underlying prices, hedging the position, and discounting risk-neutral expected payoffs.

Valuation and Risk Models then applies these tools to measure exposure, price complex positions, and test the sensitivity of portfolios to changes in rates, prices, and volatility.

• Identify bonds, equities, options, forwards, futures, swaps, and FX instruments.
• Price a bond by discounting its coupon and principal cash flows.
• Use duration and convexity to estimate bond price changes.
• Explain call and put payoff profiles at expiry.
• Value a simple option with a one-step binomial tree.

Product drill: compare bonds, equities, forwards, futures, options, and swaps. For bonds, know pricing, duration, and convexity; for options, know payoff diagrams and basic Greeks.

Market risk measures the potential loss from adverse moves in interest rates, equity prices, foreign exchange, and commodity prices. The 95% or 99% one-day Value at Risk (VaR) summarizes tail exposure, while Expected Shortfall (ES) averages losses beyond the VaR threshold. Stress testing and backtesting evaluate how the model behaves under extreme scenarios and against realized outcomes.

Credit risk arises when a counterparty or borrower fails to meet obligations. Analysts estimate default probability, migration risk through ratings, and expected losses from exposure, loss given default, and probability of default. CVA adjusts derivatives values for counterparty credit risk, and collateral, netting, and margin reduce that exposure.

Operational risk covers losses from failed processes, people, systems, and external events. Firms collect internal and external loss data, run scenario analysis, and design controls, limits, and escalation procedures to manage severity and frequency.

Liquidity risk has two dimensions: funding liquidity and market liquidity. Funding liquidity concerns the ability to meet cash obligations, while market liquidity concerns the cost and speed of unwinding positions. LCR and NSFR measure short-term resilience and stable funding, and stress tests reveal vulnerabilities in stressed funding and market conditions.

• Use VaR and ES to quantify market tail risk.
• Link PD, LGD, EAD, ratings, CVA, and collateral in credit analysis.
• Combine loss data, scenarios, and controls for operational risk.
• Distinguish funding liquidity from market liquidity.
• Apply LCR, NSFR, and liquidity stress testing.

Risk drill: know VaR and expected shortfall for market risk, exposure and default for credit risk, and event trees for operational risk. Practice stress testing and backtesting.

A realistic 4-6 week FRM Part 1 plan starts with an honest self-assessment. Take a diagnostic quiz early, rank topics by weakness, and build weekly blocks around the highest-impact areas. Aim to complete core readings in the first two weeks so later weeks can focus on application and recall.

Use spaced repetition for formulas and definitions. Review each topic after one day, again after three days, and then once per week until exam day. Short daily sessions of 60-90 minutes beat long irregular cramming because they build durable memory.

Mock exam strategy matters as much as content. Complete at least three timed mocks in exam-like conditions, review every error, and classify mistakes as knowledge gaps, calculation slips, or time pressure errors. Use that classification to adjust the next study block.

Diagnose weak areas with topic-level scores, then re-study only those sections before retesting. Practice time management by setting time budgets per question and skipping difficult items early. On exam day, arrive early, follow a familiar routine, and prioritize known formulas and question types.

• Diagnose weaknesses with a timed diagnostic before planning.
• Schedule spaced reviews at 1, 3, and 7 day intervals.
• Take at least three full timed mocks.
• Classify errors to target the right fixes.
• Protect sleep and review a one-page formula sheet the night before.

Four-to-six-week plan: weeks 1-2 core reading; weeks 3-4 practice and mocks; week 5 weak topics; week 6 final review. Use spaced repetition for formulas.