Study guide
10.1 Commodities and Commodity Derivatives: Sectors, Participants, Futures Returns & Indexes
CFA Level 2 Chartered Financial Analyst
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1 Introduction & CFA Level II Exam Strategy
1.1 CFA Level II Exam Overview & Item-Set Vignette Strategy 1.2 2026 Curriculum Structure & 10 Topic Weight Bands 1.3 300-Hour Study Plan, Calculator Mastery & Practical Skills Modules (PSM)
2 Ethical and Professional Standards
2.1 Code of Ethics & The Seven Standards of Professional Conduct 2.2 Vignette Analysis: Conflicts of Interest & Disclosure (Standard VI) 2.3 Duties to Clients (Standard III) & Duties to Employers (Standard IV) 2.4 Standard V: Investment Analysis, Recommendations & Actions and Standard VII: Responsibilities as a CFA Member or Candidate 2.5 Application of the Code and Standards: Level II Case Method 2.6 Recommended Procedures: Compliance Programs, Supervisory Systems & Firm Policies
3 Quantitative Methods
3.1 Multiple Linear Regression, ANOVA & Hypothesis Testing 3.2 Regression Diagnostics: Multicollinearity, Heteroskedasticity & Serial Correlation 3.3 Time-Series Analysis: AR Models, Nonstationarity & Cointegration 3.4 Machine Learning, Deep Learning & Big Data Projects 3.5 Extensions of Multiple Regression: Influence Analysis, Qualitative Variables & Logistic Regression 3.6 Big Data Projects: Wrangling, Exploration, Feature Engineering & Model Training
4 Economics for Valuation
4.1 Foreign Exchange Parity Relations & Equilibrium Exchange Rates 4.2 Economic Growth Theories & Production Function Analysis 4.3 Forward Points, the Carry Trade & Policy Effects on Exchange Rates 4.4 Growth Determinants: Natural Resources, Demographics, Convergence & Trade Openness
5 Financial Statement Analysis
5.1 Intercorporate Investments: Equity Method & Business Combinations 5.2 Employee Compensation: Defined Benefit Pension Plans & Share-Based Payments 5.3 Multinational Operations: Current Rate vs Temporal Translation 5.4 Analysis of Financial Institutions & The CAMELS Framework 5.5 Evaluating the Quality of Financial Reports & Accounting Adjustments 5.6 Integration of Financial Statement Analysis Techniques
6 Corporate Issuers
6.1 Analysis of Dividends: Policy Theories, Signaling & Taxation Systems 6.2 Share Repurchases: Methods, EPS and Book Value Effects & Payout Sustainability 6.3 Cost of Capital: Advanced Topics 6.4 Corporate Governance, Board Oversight & ESG Integration 6.5 Corporate Restructuring: Types, Motivations, Evaluation & Valuation 6.6 Restructuring Financial Effects: EPS, Net Debt to EBITDA, WACC & Divestitures
7 Equity Valuation
7.1 Equity Valuation Framework & Required Rate of Return Estimation 7.2 Discounted Dividend Valuation: Gordon Growth, Multistage & The H-Model 7.3 Free Cash Flow Valuation: FCFF and FCFE Modeling 7.4 Market-Based Valuation: Price and Enterprise Value Multiples 7.5 Residual Income Valuation 7.6 Private Company Valuation: Income, Market & Asset-Based Approaches 7.7 Choosing and Combining Models: Definitions of Value, Sum-of-the-Parts & Cross-Border Comparability
8 Fixed Income Analysis
8.1 The Term Structure and Interest Rate Dynamics: Spot, Forward & Swap Curves 8.2 The Arbitrage-Free Valuation Framework & Binomial Interest Rate Trees 8.3 Valuation and Analysis of Bonds with Embedded Options: OAS & Duration 8.4 Credit Analysis Models: Structural vs Reduced-Form Approaches 8.5 Credit Default Swaps (CDS): Mechanics, Pricing & Trading Strategies 8.6 Managing Yield-Curve Risk: Key Rate Durations, One-Sided Durations & Yield Volatility Structure
9 Derivatives & Risk Management
9.1 Pricing and Valuation of Forward Commitments: Equity, Fixed Income & FX 9.2 Swap Pricing and Valuation: Interest Rate, Currency & Equity Swaps 9.3 Contingent Claims: Binomial Option Pricing Model & Arbitrage Replication 9.4 The Black-Scholes-Merton (BSM) Model, Option Greeks & Dynamic Hedging 9.5 Interest Rate Options, Swaptions & the Black Model
10 Alternative Investments
10.1 Commodities and Commodity Derivatives: Sectors, Participants, Futures Returns & Indexes 10.2 Private Real Estate Investments: Direct Capitalization, DCF & Cost Approach 10.3 Public Real Estate Investments: REIT Structures, NAV, FFO & AFFO 10.4 Hedge Fund Strategies: Classification, Implementation & Portfolio Role 10.5 Real Estate Investment Features, Due Diligence & Index Construction Biases
11 Portfolio Management
11.1 Using Multifactor Models: Macroeconomic, Fundamental & Statistical Risk 11.2 Measuring and Managing Market Risk: VaR, CVaR & Stress Testing 11.3 Economics & Investment Markets: Business Cycles, Term Structure & Asset Allocation 11.4 Analysis of Active Portfolio Management & The Fundamental Law 11.5 Exchange-Traded Funds (ETFs): Creation, Arbitrage, Tracking & Applications 11.6 Backtesting and Simulation: Procedures, Metrics, Biases & Scenario Analysis
10.1 Commodities and Commodity Derivatives: Sectors, Participants, Futures Returns & Indexes
Key Takeaways Explain This Topic AI
Total return on a fully collateralized commodity futures position is the sum of price return, roll return, and collateral return, and roll return is positive in backwardation and negative in contango.
The theory of storage explains the futures basis through the cost of carry less the convenience yield, so a high convenience yield from tight inventories produces backwardation.
The insurance (hedging pressure) theory holds that producers pay a premium to hedge, so futures trade below expected spot prices and long speculators earn a risk premium.
Commodity index construction choices such as weighting by production, liquidity, or fixed weights, and the roll schedule, drive most of the return dispersion among commodity indexes.
Commodities are valued by the discounted expected future spot price rather than by discounting cash flows, because a physical commodity generates no income stream.
Last updated: August 2026
10.1 Commodities and Commodity Derivatives: Sectors, Participants, Futures Returns & Indexes
Blueprint note: Introduction to Commodities and Commodity Derivatives is one of the four Alternative Investments learning modules at Level II, alongside two real estate modules and hedge fund strategies. Vignettes present a commodity portfolio with a futures curve exhibit and ask for a return decomposition or an interpretation of the curve shape.
1. Commodity Sectors and Their Life Cycles
Sector Examples Distinguishing characteristics
Energy Crude oil, refined products, natural gas Largest sector by index weight; storable but costly; strong seasonality in natural gas; geopolitically sensitive supply
Base metals Copper, aluminium, zinc, nickel Cheap to store and non-perishable; demand tracks the industrial cycle closely; long lead times to add supply
Precious metals Gold, silver, platinum Very low storage cost relative to value; gold behaves partly as a monetary asset rather than an industrial input
Livestock Cattle, hogs Perishable; storage is effectively impossible, so the cost-of-carry link between spot and futures is weak
Grains Corn, wheat, soybeans Annual harvest cycle creates pronounced seasonality; storable between harvests with meaningful cost
Softs Coffee, sugar, cotton, cocoa Concentrated geographically, so weather in one region drives global prices
Life cycle — the path from production through trading or consumption — differs in ways that matter for the futures curve:
Grains have a single harvest and then a drawdown, so the curve typically shows a step at each new crop year and inventories are highest immediately after harvest.
Natural gas is produced continuously but consumed seasonally, so storage cycles produce a repeating seasonal curve shape with injection and withdrawal seasons.
Livestock cannot be stored, so the futures curve reflects expected production timing rather than carry.
Metals have continuous production and consumption with large above-ground inventories, so their curves are the closest to a clean cost-of-carry relationship.
Valuation contrast. Equities and bonds are valued by discounting the cash flows they generate. A physical commodity generates no cash flow and in fact costs money to hold, so it is valued as the discounted expected future spot price , with the futures price serving as the market's observable estimate.
2. Participants in Commodity Futures Markets
- Participant Motivation Typical position
- Hedgers (producers) Lock in a selling price for future output Short futures
- Hedgers (consumers) Lock in an input cost Long futures
Informed investors and speculators Take directional or relative-value risk for return Either side; provide liquidity to hedgers
Liquidity providers / market makers Earn the bid–ask spread and short-term imbalances Flat over time, intraday either side
- Arbitrageurs Exploit deviations between spot, futures, and storage costs Offsetting cash-and-carry positions
- Analysts and exchanges Do not take positions; set rules, margins, and disseminate information None
The balance between producer and consumer hedging is what the hedging pressure hypothesis rests on: when producers hedge more than consumers, the net short hedging position must be absorbed by long speculators, who require compensation.
- 3. Contango, Backwardation, and the Three Theories
- The shape of the curve
Backwardation : futures price below the current spot price; the curve slopes downward. A long futures position rolling forward buys the next contract at a lower price than the one it sells, generating a positive roll return .
Contango : futures price above the current spot price; the curve slopes upward. Rolling forward generates a negative roll return .
Theory 1 — Insurance (hedging pressure)
Keynes's normal backwardation argument: producers are net hedgers and pay a premium to transfer price risk. Consequently the futures price sits below the expected future spot price , and long speculators earn that discount as a risk premium as the futures price converges to spot. The prediction is that markets are normally backwardated. The empirical objection is that consumers also hedge, and in several markets they hedge more, which produces contango.
Theory 2 — Theory of storage
The futures price is determined by the cost of carry net of the convenience yield : Futures price = Spot price + Storage costs + Financing costs − Convenience yield \text{Futures price} = \text{Spot price} + \text{Storage costs} + \text{Financing costs} - \text{Convenience yield} Futures price = Spot price + Storage costs + Financing costs − Convenience yield
The convenience yield is the non-monetary benefit of holding the physical commodity — the option to keep a plant running through a supply interruption. It rises when inventories are low and falls to near zero when inventories are ample.
Low inventories → high convenience yield → backwardation.
High inventories → low convenience yield → contango.
This theory explains why the same commodity moves between the two states as the inventory cycle turns, which the insurance theory cannot.
Theory 3 — Hedging pressure hypothesis
A generalisation of the insurance theory: the curve's shape depends on the net balance of producer and consumer hedging demand. When producers dominate, backwardation; when consumers dominate, contango. It accommodates the observed regime switching that pure normal backwardation does not.
4. Decomposing the Total Return
For a fully collateralized long futures position: Total return = Price return + Roll return + Collateral return \text{Total return} = \text{Price return} + \text{Roll return} + \text{Collateral return} Total return = Price return + Roll return + Collateral return
- Component Definition Driver
- Price return Change in the price of the front futures contract over the period Spot price movement
Roll return Gain or loss from closing the expiring contract and opening the next The shape of the curve : positive in backwardation, negative in contango
Collateral return Return on the cash posted to collateralize the notional The risk-free rate, since a fully collateralized position holds the full notional in bills Roll return = Near contract price − Far contract price Near contract price × Proportion of position rolled \text{Roll return} = \frac{\text{Near contract price} - \text{Far contract price}}{\text{Near contract price}} \times \text{Proportion of position rolled} Roll return = Near contract price Near contract price − Far contract price × Proportion of position rolled
Worked example. An investor holds a fully collateralized long position in a commodity. Over the year:
The front contract price rises from 82.00 to 88.00.
At each quarterly roll the near contract trades at 88.00 and the next contract at 89.76, so the position is rolled at a 2.0% loss each time, on 100% of the position, four times a year.
Treasury bills yield 4.3%.
Computing:
Price return: ( 88.00 − 82.00 ) / 82.00 = +7.32% (88.00 - 82.00)/82.00 = \textbf{+7.32\%} ( 88.00 − 82.00 ) /82.00 = +7.32%
- Roll return: − 2.0 % × 4 = -8.00% -2.0\% \times 4 = \textbf{-8.00\%} − 2.0% × 4 = -8.00%
- Collateral return: +4.30% \textbf{+4.30\%} +4.30%
Total return: 7.32 % − 8.00 % + 4.30 % = + 3.62 % 7.32\% - 8.00\% + 4.30\% = +3.62\% 7.32% − 8.00% + 4.30% = + 3.62%
The commodity's price rose 7.32%, but the investor earned only 3.62%, because a persistently contangoed curve consumed most of the gain. This wedge between spot price performance and investable index performance is the most-tested idea in the module, and it explains why long-only commodity index returns have repeatedly lagged headline spot prices.
Under backwardation the arithmetic reverses: a flat spot price with a curve rolling at +1.5% per quarter produces a positive total return of about 6% plus the collateral yield, from curve shape alone.
5. Commodity Swaps
A commodity swap exchanges a series of payments based on a commodity price. The main structures:
Structure Mechanics User
Excess return swap One leg pays the commodity's excess return (price plus roll), the other a fixed rate An investor seeking exposure without managing the futures roll
Total return swap As above, plus the collateral return An investor replicating a fully collateralized index position
Basis swap Exchanges the price differential between two grades, locations, or delivery months A refiner hedging a crude quality or location differential
Variance / volatility swap Settles on realised variance or volatility rather than price A hedger of price uncertainty rather than price level
Swaps let an institution obtain index exposure without operational responsibility for rolling contracts and posting margin, at the cost of counterparty credit exposure to the dealer.
6. Commodity Index Construction and Why It Matters
Index construction choices produce large differences in realised returns even when the underlying commodities are identical.
Choice Options Effect
Constituent selection Broad versus energy-heavy The single largest driver of return dispersion, because energy dominates aggregate commodity volatility
Weighting scheme World production, liquidity, fixed equal weights Production weighting concentrates in energy; equal weighting tilts toward agriculture and metals
Rolling methodology Front-month roll; deferred-month roll; dynamic roll that selects the contract minimising contango Determines how much of the negative roll return in a contangoed market the index actually incurs
Rebalancing frequency Monthly, quarterly, annual Frequent rebalancing generates a rebalancing return by systematically selling appreciated and buying depreciated constituents
Two consequences the exam draws out:
An index tracking a commodity is not the commodity. An investor cannot capture the spot price return without incurring the roll, so index returns and spot price changes diverge, sometimes by a great deal, over multi-year horizons.
Diversification within the index and the rebalancing effect mean a commodity index can produce a positive return even when the average constituent's price return is close to zero, because volatile, imperfectly correlated constituents that are periodically rebalanced generate a rebalancing premium.
Level II traps in the commodities module
Assuming backwardation means a falling price. It describes the shape of the curve, not the direction of the spot price.
Forgetting the collateral return in a total-return decomposition.
Reading a positive price return as a positive total return in a contangoed market.
Attaching the convenience yield to high inventories; it is high when inventories are low .
Valuing a commodity by discounting cash flows; a physical commodity has none, so it is valued as the discounted expected future spot price.