FRM Part I study guide
Every chapter of all four papers, broken into 373 topics. Each topic shows the concept, a step-by-step way to solve questions, the quickest method for the exam, the mistakes students make and worked examples.
Paper 1: FRM Exam Part I
Objective, MCQ · 62 chaptersModern Portfolio Theory (MPT) and the Capital Asset Pricing Model (CAPM)
- Portfolio Risk and Return, Diversification
- Markowitz Efficient Frontier and Optimal Portfolios
- Capital Market Line and Two-Fund Separation
- CAPM and the Security Market Line
- Performance Measures: Sharpe, Treynor, Jensen, Information Ratio
- Single-Index Model, Market Model and Regression
- CAPM Limitations and Extensions
Learning From Financial Disasters
- Lessons from Financial Disasters: Overview
- Interest Rate Risk: Savings and Loan Crisis and Orange County
- Funding Liquidity Risk: Metallgesellschaft, LTCM, Lehman
- Rogue Trading and Operational Failures
- Model Risk and Valuation Failures
- Financial Crisis of 2007-2009 and Securitization
- Governance, Reputation and Compliance Failures
Anatomy of the Great Financial Crisis of 2007-2009
- Origins of the 2007-2009 Financial Crisis
- Securitization and the Originate-to-Distribute Model
- Credit Rating Agencies and Structured Product Failures
- Shadow Banking, Leverage and Liquidity Runs
- Crisis Events and Institutional Failures
- Amplification Mechanisms and Systemic Risk
- Policy Responses and Regulatory Lessons
Linear Regression
- Simple Linear Regression Model and OLS
- OLS Assumptions and Properties of Estimators
- Hypothesis Testing and Confidence Intervals for Coefficients
- Goodness of Fit: R-squared, ESS, TSS, SSR
- Multiple Regression and Joint Hypothesis Tests
- Heteroskedasticity, Multicollinearity and Model Misspecification
- Dummy Variables and Regression Interpretation
Machine-Learning Methods
- Overview of Machine Learning and Types of Learning
- Overfitting, Bias-Variance Tradeoff and Model Validation
- Data Preparation and Feature Engineering
- Regularization: Ridge, LASSO and Elastic Net
- Logistic Regression and Classification Metrics
- Decision Trees, Ensembles and K-Nearest Neighbors
- Unsupervised Learning: Clustering and PCA
- Neural Networks and Deep Learning
Machine Learning and Prediction
- Machine Learning Basics and Types of Learning
- Overfitting, Bias-Variance Tradeoff and Data Splitting
- Regularization: Ridge, LASSO and Elastic Net
- Dimension Reduction and Principal Components Analysis
- Clustering: K-Means and Hierarchical Methods
- Decision Trees, Ensembles and Random Forests
- Neural Networks and Deep Learning
- Model Evaluation and Classification Metrics
Futures Markets
- Futures Contract Basics and Market Structure
- Margin, Marking to Market and Daily Settlement
- Clearinghouses and Counterparty Risk
- Delivery, Settlement and Order Types
- Hedging with Futures and Basis Risk
- Stock Index Futures and Beta Hedging
- Interest Rate Futures and Treasury Futures
- Futures Pricing, Rolling and Hedge Accounting Issues
Pricing Financial Forwards and Futures
- Forward and Futures Contracts Basics
- Cost of Carry and Forward Pricing Without Income
- Forwards on Assets with Known Income or Yield
- Storage Costs, Convenience Yield and Commodity Forwards
- Futures Prices, Expected Spot Prices and Valuation of Forwards
- Treasury Bond Futures and Eurodollar Futures
- Hedging with Futures and Optimal Hedge Ratio