How to build a derivatives course: a complete guide for lecturers

Central question: What makes a derivative valuable, and how does no-arbitrage connect contract terms to underlying prices?

Start by giving students a map of the market before asking them to price anything. They need to distinguish exchange-traded and OTC contracts, long and short positions, physical and cash settlement, notional amount, margin, counterparty exposure and the difference between hedging, speculation and arbitrage. No-arbitrage then becomes the organising principle that links these mechanics to valuation.

What this concept covers

• Forwards, futures, swaps and options as contingent or future claims.
• Exchange-traded versus OTC markets, clearing, margin and settlement.
• Long and short exposures, notionals, cash flows and payoff diagrams.
• Hedging, speculation and arbitrage as different economic motives.
• Law of one price, replication and no-arbitrage reasoning.

Learning outcomes

By the end of this concept, students should be able to:

• Classify the main derivative contract families and their settlement mechanics.
• Distinguish price, value, payoff and notional amount.
• Explain why margin and collateral matter even when the initial contract value is near zero.
• Use no-arbitrage logic to identify inconsistent prices.
• Separate a genuine hedge from a directional speculative position.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Use a contract-classification exercise before any formulas.
• Draw cash-flow timelines for a forward, futures contract, swap and option.
• Give two economically equivalent portfolios and ask whether their prices can differ.
• Ask students to label the risk transferred in each contract.

Case-style example

HarbourTech expects to receive EUR 8 million in six months. Spot EUR/GBP is 0.8550, the six-month forward is 0.8615 and the treasurer can also leave the exposure unhedged. Students compute the sterling receipts under EUR/GBP outcomes of 0.80, 0.86 and 0.92, then classify each choice as hedging or speculation. Two defensible conclusions emerge because certainty protects the budget while remaining open preserves upside. Students discover that the purpose of a derivative determines whether the same payoff is prudent or risky.

Where students struggle

Students often treat every derivative as a bet. The key correction is to make the underlying exposure explicit first. A short forward against a future foreign-currency receipt can reduce risk even though the contract itself has a linear payoff.

Reading and a quick check

Pair Hull, Chapters 1-2, with the 2025 BIS derivatives-market primer. Quick check: give three positions and ask students to identify the underlying exposure, contract payoff and economic purpose.

Where it leads

With the market map in place, students can value the simplest linear contracts: forwards and futures.

Central question: How do spot prices, financing, income and storage costs determine a forward or futures price?

Forward pricing is the cleanest place to make no-arbitrage operational. Students should see the cash-and-carry trade, understand why a forward price is not a forecast of the future spot price, and learn how dividends, foreign interest rates, convenience yield and storage costs alter the simple financing relationship. Basis then connects theoretical pricing to real hedging outcomes.

What this concept covers

• Forward value versus delivery price.
• Cash-and-carry and reverse cash-and-carry arbitrage.
• Equity index, currency and commodity forward pricing.
• Futures marking-to-market and the forward-futures distinction.
• Basis, convergence and sources of basis risk.

Learning outcomes

By the end of this concept, students should be able to:

• Derive a fair forward price from spot and carrying costs.
• Calculate the value of an existing forward after market prices change.
• Explain how income or foreign interest rates alter forward pricing.
• Diagnose apparent arbitrage opportunities.
• Interpret basis and convergence near expiry.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Build the cash-and-carry trade line by line on the board.
• Use a currency forward to show why two interest rates matter.
• Compare a commodity with storage costs to an equity index with dividend yield.
• Make students state the financing trade before writing the formula.

Case-style example

Northstar Metals trades at $72 per unit, annual financing is 5%, storage costs are $1.20 paid upfront and the one-year futures price is $78.80. Students calculate the theoretical futures price, design a cash-and-carry trade if the quoted price is too high, then discuss transaction costs of $1.50 per unit. One group may call the quote mispriced while another rejects the trade after costs. Students discover that no-arbitrage gives a band in real markets, not a frictionless point estimate.

Where students struggle

Students often interpret the forward price as the market expectation of the future spot price. Keep valuation and forecasting separate: the no-arbitrage forward price follows from replicating cash flows, while expected future spot prices depend on risk premia and beliefs.

Reading and a quick check

Use Hull, Chapters 5-6. Quick check: ask why the forward price of a dividend-paying stock can sit below the simple spot compounded at the risk-free rate.

Where it leads

Once students can price a futures contract, they can ask the corporate or portfolio question: how many contracts should be used to hedge an exposure?

Central question: How should a hedger choose contract direction, hedge ratio, maturity and underlying when the exposure is imperfect?

This concept turns linear derivative pricing into risk management. The teaching objective is not merely to calculate the number of futures contracts. Students should identify the underlying exposure, distinguish price risk from basis risk, choose a contract that matches the exposure and recognise that a minimum-variance hedge can differ from a one-for-one hedge.

What this concept covers

• Long and short hedges for commodity, equity, interest-rate and FX exposures.
• Optimal and minimum-variance hedge ratios.
• Cross-hedging when the exact underlying is unavailable.
• Rolling hedges, maturity mismatch and basis risk.
• Hedge effectiveness and the trade-off between certainty and opportunity.

Learning outcomes

By the end of this concept, students should be able to:

• Choose the correct hedge direction for a real exposure.
• Calculate a simple and minimum-variance hedge ratio.
• Estimate the number of futures contracts required.
• Explain residual basis and cross-hedge risk.
• Evaluate hedge effectiveness after the fact.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Start with the exposure before showing contract quotes.
• Use a cross-hedge example where correlation is materially below one.
• Ask students to compare accounting certainty with economic risk reduction.
• Have groups defend different hedge ratios rather than search for one universal answer.

Case-style example

SkyHarvest Airlines expects to buy 9 million gallons of jet fuel in four months. Jet fuel futures are unavailable, but heating-oil futures represent 42,000 gallons each; estimated price volatility is 18% for jet fuel and 16% for heating oil, with correlation 0.82. Students compute the minimum-variance hedge ratio and contract count, then compare it with a one-for-one hedge. Students discover that the statistically optimal hedge can leave managers uncomfortable because operational exposure and basis behaviour still matter.

Where students struggle

Students can calculate a hedge ratio while losing sight of what is being hedged. Require every calculation to be preceded by a sentence that states the economic exposure and the direction in which an adverse price move hurts the firm.

Reading and a quick check

Use Hull, Chapter 3, and Carter + Smith as an applied FX hedge case later in the course. Quick check: ask when a cross-hedge with a lower correlation can still be preferable to leaving the exposure open.

Where it leads

Linear hedging leads naturally to swaps, which transform a stream of future cash flows rather than a single settlement date.

Central question: How can firms transform interest-rate or currency exposure through a sequence of linked cash-flow exchanges?

Swaps help students connect derivatives to treasury, financing and counterparty choice. Teach the fixed-for-floating interest-rate swap first, then extend to currency swaps and the role of discount curves. The course should emphasise that a swap is a package of future cash flows whose value changes over time, not simply a fixed rate quoted at inception.

What this concept covers

• Plain-vanilla interest-rate swaps and par swap rates.
• Fixed versus floating debt exposure.
• Currency swaps and cross-currency cash flows.
• Valuation after inception using discounted cash flows.
• Counterparty exposure, collateral and Credit Support Annex basics.

Learning outcomes

By the end of this concept, students should be able to:

• Construct the cash flows of a fixed-for-floating swap.
• Estimate a par swap rate from discount factors or forward rates.
• Value a swap after market rates move.
• Explain how a swap changes the economic profile of debt without refinancing it.
• Identify counterparty and collateral considerations.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Use a debt decision first, then introduce the swap as the transformation layer.
• Build a swap as a portfolio of forward-rate agreements or bonds.
• Ask students to map who benefits when rates rise.
• Use a current yield curve rather than a memorised flat-rate example.

Case-style example

Orion Data has $400 million of five-year floating-rate debt at SOFR + 120 bps. It can enter a pay-fixed, receive-SOFR swap at 4.05%, while management expects policy rates to fall toward 3.0% within two years. Students compute the synthetic fixed cost, compare it with remaining floating, and recommend how much of the debt to swap. Students discover that the best hedge is not necessarily 100% because budget certainty, rate views and refinancing flexibility create defensible competing choices.

Where students struggle

Students often think a swap eliminates risk. It exchanges one exposure for another and introduces counterparty, collateral and liquidity considerations. Keep the gross cash flows visible before netting.

Reading and a quick check

Use Hull, Chapters 4 and 7. Pair with the 2024 Darden Google debt case for a modern interest-rate swap application.

Where a simulation fits

The Investment Banking Simulation is the strongest available Finsimco fit here. In Round 2, teams providing derivatives solutions prepare currency-hedging offers specifying position, amount, exchange rate and Credit Support Annex terms, while PE teams evaluate financing and derivative packages together. It is best used after students know swap and hedging mechanics, not as a substitute for pricing instruction.

Where it leads

Swaps are linear instruments. Options introduce nonlinearity and make state-contingent risk transfer explicit.

Central question: How do option rights create nonlinear payoffs, and how can simple options be combined to shape risk?

Students should become fluent in option payoffs before seeing Black-Scholes-Merton. Calls and puts, long and short positions, intrinsic value, time value, moneyness, put-call parity and strategy diagrams give them the intuition required for later pricing and hedging. The priority is to make the payoff logic explainable without relying on software.

What this concept covers

• European and American calls and puts.
• Intrinsic value, time value and moneyness.
• Upper and lower bounds and early-exercise intuition.
• Put-call parity and synthetic positions.
• Protective puts, covered calls, spreads, straddles, strangles and collars.

Learning outcomes

By the end of this concept, students should be able to:

• Draw and interpret option payoff and profit diagrams.
• Use put-call parity to construct synthetic positions.
• Identify arbitrage when option bounds are violated.
• Compare hedging and speculative uses of common strategies.
• Explain how volatility views differ from directional views.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Make students draw payoffs from first principles before showing strategy names.
• Use put-call parity as a replication exercise.
• Give the same market view to different groups and ask them to build different structures.
• Separate payoff at expiry from current option value.

Case-style example

MiraBio trades at $50. A three-month 50-strike call costs $3.20 and the 50-strike put costs $2.90. A fund expects a major clinical announcement but has no strong view on direction. Students calculate the cost and break-even points of a long straddle, compare it with buying only the call, and show outcomes at $35, $48, $50, $58 and $70. Students discover that being right about volatility can still lose money if the realised move is smaller than the option premium implies.

Where students struggle

Students confuse payoff, profit and option value before expiry. Require labels on every graph and use a simple rule: expiry payoff comes from the contract; profit also subtracts or adds the premium; current value depends on time and volatility.

Reading and a quick check

Use Hull, Chapters 10-12. Quick check: ask students to replicate a long call using a put, stock and financing through put-call parity.

Where it leads

Once students understand option payoffs, they can price them by replication in a one-period and multi-period binomial model.

Central question: How can a derivative be priced by building a portfolio that replicates its future payoff state by state?

The binomial model is the conceptual bridge between no-arbitrage and continuous-time option pricing. It makes delta, replication and risk-neutral valuation visible. Students should be able to price a one-period European option by solving for the replicating portfolio before moving to risk-neutral probabilities and multi-step trees.

What this concept covers

• One-period stock-price trees and option payoffs.
• Replicating portfolios using stock and borrowing or lending.
• Risk-neutral probabilities and discounted expected payoffs.
• Multi-step trees and backward induction.
• American exercise decisions within a tree.

Learning outcomes

By the end of this concept, students should be able to:

• Construct a binomial price tree.
• Solve for the hedge ratio and cash position in a replicating portfolio.
• Price an option with risk-neutral probabilities.
• Explain why real-world expected returns are not required for no-arbitrage pricing.
• Identify early-exercise decisions for American options.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Solve the same option twice: replication first, risk-neutral probability second.
• Ask students to reconcile why the two methods must agree.
• Use a two-step tree to make rebalancing visible.
• Introduce American exercise only after European pricing is secure.

Case-style example

Kestrel Systems trades at $100. In one year it will be either $130 or $80. The risk-free rate is 4% and a European call has strike $105. Students calculate the call payoffs, solve the replicating stock and bond positions, then price the option using the risk-neutral probability. A second group is asked whether a $19 market price creates arbitrage. Students discover that the price comes from replication, not from guessing the probability of the up state.

Where students struggle

Students often treat the risk-neutral probability as a forecast. Repeatedly state that it is a pricing device implied by no-arbitrage and the traded assets in the model.

Reading and a quick check

Use Hull, Chapter 13. Quick check: ask which input in a one-period binomial tree changes when the actual expected stock return changes, and why the option price need not.

Where it leads

The multi-step replication logic becomes the intuition for Black-Scholes-Merton and continuous dynamic hedging.

Central question: What does Black-Scholes-Merton price, why does the formula work, and when do its assumptions become fragile?

Teach Black-Scholes-Merton as a model of replication and risk-neutral valuation, not as a formula to memorise. Students should know the inputs, signs of comparative statics, economic meaning of d1 and d2 at an intuitive level, and the assumptions that allow continuous rebalancing. They should also see immediately why real markets generate volatility smiles and model risk.

What this concept covers

• Black-Scholes-Merton inputs and European option valuation.
• Risk-neutral valuation and continuous replication intuition.
• Dividend yields and currency options.
• Comparative statics across spot, strike, time, rates and volatility.
• Assumptions, limitations and model risk.

Learning outcomes

By the end of this concept, students should be able to:

• Calculate or interpret a Black-Scholes-Merton option value.
• Predict how an option value changes when each input changes.
• Explain the role of volatility as a pricing input.
• Connect the model to dynamic replication.
• Critique the assumptions using observed market behaviour.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Use a spreadsheet or code implementation only after students can predict input effects.
• Run one sensitivity at a time and ask for intuition before numbers.
• Compare model prices with observed implied volatilities.
• Make model limitations part of the assessed answer.

Case-style example

Vertex Pharma trades at $84. A six-month call has strike $80, the continuously compounded risk-free rate is 3.5%, dividend yield is 1% and annual volatility is 28%. Students use a supplied Black-Scholes-Merton sheet to value the call, then reprice at 20% and 40% volatility and after a 10% fall in the stock. One group argues the option is expensive at the market quote; another defends the premium because event risk is not captured by constant volatility. Students discover that model disagreement is often assumption disagreement.

Where students struggle

Students can produce a price without being able to explain it. Ask for directional sensitivity before exact calculation and require a written sentence on which assumption is least credible for the underlying being analysed.

Reading and a quick check

Use Hull, Chapters 14-15. Quick check: ask why the expected return on the stock does not appear in the Black-Scholes-Merton formula.

Where it leads

Pricing identifies value. Greeks identify how that value changes as the market moves, which is the basis of dynamic hedging.

Central question: How should an options book be hedged when its sensitivities change with prices, volatility and time?

Greeks turn option pricing into a risk-management system. Delta is the natural starting point, but students should quickly see why delta changes with gamma, why vega matters when implied volatility moves, and why theta reflects the cost or benefit of time decay. Dynamic hedging should be taught as an imperfect process with rebalancing costs and discrete jumps.

What this concept covers

• Delta, gamma, vega, theta and rho.
• Delta-neutral portfolios and rebalancing.
• Gamma and vega exposure in nonlinear books.
• Discrete versus continuous hedging.
• P&L attribution and hedging error.

Learning outcomes

By the end of this concept, students should be able to:

• Interpret the sign and magnitude of core Greeks.
• Construct a delta-neutral hedge.
• Calculate how the hedge must change after a price move.
• Explain why gamma creates rebalancing demand.
• Evaluate hedging error from discrete moves and volatility changes.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Give students a small option book and require a risk report before any trade.
• Use a two-stage price move to force re-hedging.
• Compare high-gamma and low-gamma positions.
• Debrief why a delta-neutral book can still lose money.

Case-style example

Aurum Options is short 2,000 calls on a stock trading at $60. Each call has delta 0.55 and gamma 0.035. Students calculate the share hedge, then recompute approximate delta after the stock jumps to $64 and determine the additional shares needed. The desk can rebalance immediately with a 12 bps trading cost or wait until end of day. Students debate the choice. They discover that dynamic hedging converts option convexity into a sequence of real trading decisions with cost and gap risk.

Where students struggle

Students often think delta neutrality means risk neutrality. Make them state what remains after delta is hedged: gamma, vega, theta, jump risk, basis risk, liquidity and model error.

Reading and a quick check

Use Hull, Chapter 19, then connect to Pine Street Capital for delta-hedging application. Quick check: ask why a short-gamma book tends to buy after prices rise and sell after prices fall when it is re-hedged.

Where it leads

Greeks depend on volatility assumptions. The next concept treats implied volatility as a market object in its own right.

Central question: What information is embedded in option prices when implied volatility differs across strikes and maturities?

A modern derivatives course should move beyond a single volatility input. Students need to read an implied-volatility surface, understand skew and term structure, distinguish realised from implied volatility, and recognise that option prices reflect crash risk, supply-demand effects, liquidity and event risk as well as textbook diffusion assumptions.

What this concept covers

• Implied versus historical or realised volatility.
• Volatility smiles, skews and term structures.
• Volatility surfaces across strike and maturity.
• Variance and volatility risk premia at an intuitive level.
• Event risk, jump risk and volatility trading strategies.

Learning outcomes

By the end of this concept, students should be able to:

• Back out and interpret implied volatility conceptually or with software.
• Read a volatility skew and explain what it means for option prices.
• Compare implied and realised volatility.
• Identify when a strategy is primarily a volatility view.
• Explain why one constant volatility cannot fit observed option prices.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Use a real option chain and plot implied volatility by strike.
• Ask students to explain skew before introducing advanced stochastic-volatility models.
• Compare an earnings-event expiry with a nearby non-event expiry.
• Make transaction costs part of every volatility-trading discussion.

Case-style example

Cobalt Cloud reports earnings in 12 days. One-month at-the-money implied volatility is 54%, three-month implied volatility is 36%, and the stock has realised 28% volatility over the previous year. Students compare a long one-month straddle with selling the same structure, estimate the approximate move implied by the short-dated volatility and list what could invalidate a realised-versus-implied comparison. Students discover that a high implied volatility can be rational when uncertainty is concentrated around a known event.

Where students struggle

Students interpret high implied volatility as a simple forecast that realised volatility will be equally high. Emphasise that implied volatility is the volatility input that reconciles a model with the market price and can contain risk premia and demand effects.

Reading and a quick check

Pair Hull, Chapter 20, with Zhan et al. (2022) and Bali et al. (2023) for advanced cohorts. Quick check: ask what a steep downside skew says about the relative price of crash protection.

Where it leads

Volatility concepts prepare students for more complex fixed-income and credit derivatives where curve shape, optionality and counterparty risk interact.

Central question: How do futures, swaps and options on rates transfer exposure to the level, slope and volatility of the yield curve?

Interest-rate derivatives pull together forwards, swaps, option pricing and term-structure thinking. Keep the first objective practical: show how SOFR futures, forward rates, swaps, caps, floors and swaptions relate to a borrower or investor exposure. Advanced cohorts can extend to curve construction and market models without turning the whole course into fixed-income engineering.

What this concept covers

• Forward rates and SOFR or money-market futures.
• Interest-rate swaps and curve discounting.
• Caps, floors and swaptions.
• Duration, convexity and hedge intuition.
• Basis risk and the post-Libor move to overnight risk-free rates.

Learning outcomes

By the end of this concept, students should be able to:

• Interpret forward rates from a term structure.
• Use rate futures or swaps to hedge an interest-rate exposure.
• Explain the payoff of caps, floors and swaptions.
• Identify curve and basis risks that remain after a hedge.
• Connect benchmark reform to current derivative-market structure.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Begin with a borrower choosing fixed, floating or swapped debt.
• Use a simple forward-rate curve rather than assuming a flat yield curve.
• Compare a swap hedge with a cap to show certainty versus retained upside.
• For MSc students, add curve bootstrapping as a modelling workshop.

Case-style example

Granite Housing has £250 million of floating-rate debt resetting quarterly for three years. A pay-fixed swap is quoted at 3.75%, while a three-year cap with strike 4.25% costs 1.8% of notional upfront. Students compare annualised cash-flow protection if rates fall to 2.5%, stay near 4% or rise to 6%, then recommend a hedge. Students discover that the swap offers budget certainty while the cap behaves like insurance and preserves benefit from falling rates at an upfront cost.

Where students struggle

Students can confuse a forward rate with a forecast and a swap rate with a single-period rate. Keep the sequence of discount factors and future reset dates explicit.

Reading and a quick check

Use Hull, Chapters 4, 6, 7 and selected material from Chapters 29-34. Pair with Ehlers and Todorov (2025) on the post-Libor interest-rate derivatives market.

Where it leads

Rate derivatives raise the importance of collateral, clearing and counterparty exposure, which also sit at the centre of credit derivatives.

Central question: How do derivative contracts transfer credit risk, and how do collateral and clearing alter counterparty exposure?

This concept prevents students from treating derivatives as pure market-risk instruments. Credit default swaps show direct transfer of default risk, while every OTC derivative also creates counterparty exposure that evolves with market value. Teach netting, collateral, initial and variation margin, central clearing and XVA at the level appropriate to the cohort.

What this concept covers

• Credit default swap protection buyer and seller economics.
• Credit spreads, default events and recovery.
• Current and potential future counterparty exposure.
• Netting, collateral, Credit Support Annexes and margin.
• Central counterparties, initial margin, variation margin and liquidity risk.

Learning outcomes

By the end of this concept, students should be able to:

• Explain CDS cash flows and the economic meaning of a credit spread.
• Distinguish reference-entity credit risk from counterparty credit risk.
• Explain how netting and collateral reduce exposure.
• Describe why margin calls can create liquidity pressure.
• Evaluate the trade-off between bilateral and centrally cleared structures.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Use a simple CDS payoff before any hazard-rate model.
• Map a bilateral swap exposure before and after collateral.
• Run a margin-call stress scenario.
• Ask students who bears what risk after central clearing, rather than saying the risk disappears.

Case-style example

Redwood Bank has a positive $18 million market value on an OTC swap with Nova Energy. The CSA requires daily variation margin with a $2 million threshold, and Nova has posted $14 million. Overnight the swap moves another $7 million in Redwood’s favour while Nova’s credit spread widens sharply. Students calculate unsecured exposure before and after the margin call, then debate whether collateral has solved the problem. Students discover that collateral reduces credit exposure but can turn market moves into urgent liquidity demands.

Where students struggle

Students can say clearing removes counterparty risk. It redistributes and manages that risk through CCP rules, margin, default funds and member obligations. Liquidity risk can rise precisely when market risk rises.

Reading and a quick check

Use Hull, Chapters 9 and 24-25, together with the 2025 BCBS-CPMI-IOSCO margin report and the 2026 ISDA Margin Survey.

Where a simulation fits

The Investment Banking Simulation fits selectively because its derivatives offers include Credit Support Annex terms and are evaluated alongside financing packages. Use the debrief to distinguish market exposure, counterparty exposure and collateral terms. The simulation does not replace dedicated CDS pricing or clearing instruction.

Where it leads

The final concept integrates instrument choice, model risk, governance and hedge evaluation across a portfolio or firm.

Central question: When is a derivative strategy economically sound, governable and defensible after accounting for liquidity, model and behavioural risk?

Close the course by moving from individual instruments to a policy decision. Students should select a derivative because it changes an identified exposure in a useful way, not because it is mathematically sophisticated. This is where hedge effectiveness, limits, liquidity, collateral, model risk, accounting implications, governance and post-trade monitoring come together.

What this concept covers

• Risk identification before instrument selection.
• Hedge policy, limits, mandates and escalation.
• Model validation, stress testing and scenario analysis.
• Liquidity, collateral and rollover risk.
• Performance attribution, hedge effectiveness, conduct and ethics.

Learning outcomes

By the end of this concept, students should be able to:

• Design a derivatives strategy that matches a stated risk appetite.
• Compare alternative instruments on economic rather than headline cost alone.
• Stress-test a strategy for price, volatility, basis and liquidity shocks.
• Identify model-risk and governance controls.
• Defend a hedge or trading recommendation to a risk committee.

Supports the course learning outcomes above and produces evidence that can be assessed.

How to teach it

• Use a capstone risk memo with multiple instrument choices.
• Require students to state what risk remains after the hedge.
• Include a stress event that creates collateral or liquidity pressure.
• Mark the defence of assumptions more heavily than spreadsheet polish.

Case-style example

Atlas Pension Fund holds a $600 million equity portfolio and expects a volatile quarter. The CIO can buy a three-month 95% index put for 2.4% of protected notional, reduce equity exposure by 15%, or leave the portfolio unchanged. The board requires a maximum one-quarter loss tolerance of 12% but dislikes visible premium expense. Students compare downside, cost, basis risk and governance implications, then defend one policy. Students discover that risk management is a mandate decision, not simply an option-pricing exercise.

Where students struggle

Students can optimise a hedge ratio without testing whether the strategy is consistent with mandate, liquidity and governance. The capstone should require a written residual-risk statement and a decision rule for when the hedge would be changed.

Reading and a quick check

Use Hull, Chapters 22-23 and 37, then revisit the BIS and ISDA readings. Quick check: ask students to list five risks that remain after a mathematically delta-neutral or notionally matched hedge.

Where a simulation fits

The Portfolio Management Simulation is an adjacent rather than direct derivatives fit. It gives students a live portfolio-risk baseline using beta, covariance, volatility, Alpha, Sharpe Ratio and rebalancing. A lecturer can run it before the capstone and then ask how index futures or options would alter the observed exposure. The platform itself does not ask students to trade derivatives. The Investment Banking Simulation can also support the governance and counterparty side of the capstone.

Where it leads

The course closes with students able to connect instrument mechanics, pricing, hedging and governance into one defensible risk decision.

10-week course structure

A 10-session version should compress adjacent concepts rather than remove the pricing-to-risk lifecycle. Combine forward pricing with futures hedging, and combine credit derivatives with counterparty risk and final governance.

14-week course structure

A 14-session version creates room for more quantitative depth and a stronger capstone. Use the extra sessions for numerical methods or exotic options, a deeper market-infrastructure block and a dedicated risk-committee presentation.

The Investment Banking Simulation is a 16-32 hour, web-based, team simulation spanning a 20-year lifecycle of two companies. It is broader than a Derivatives course, but it contains an unusually relevant derivatives strand: Round 2 combines debt capital markets and derivatives, and later rounds connect those risk decisions to advisory, valuation and restructuring.

Why it fits here

Use it after students understand FX forwards or swaps, debt financing and counterparty terms. The teaching value is the interaction between financing and hedging rather than standalone option-pricing practice.

What students do

• Review company background, historical financials, management forecasts and existing financing.
• In Round 2, lender teams prepare detailed debt packages covering amount, interest premium, covenants, maturity, amortisation, guarantees and security.
• Teams providing derivatives solutions prepare offers covering position, amount, exchange rate and Credit Support Annex terms.
• PE firm teams review the available financing and derivative packages, select counterparties, update models and submit enterprise-value bids and Approval Memoranda.
• Across later rounds, teams work through broader advisory, valuation, fairness-opinion and restructuring decisions.

Recommended course placement

• After Session 4 if derivatives terms are the main debrief focus.
• Across a longer capstone block if the course can support the full 16-32 hour format.
• Revisit outcomes after Session 11 to discuss CSA, counterparty and collateral logic.

Knowledge students should have before the simulation

• Basic finance and DCF valuation, which the live product page lists as prerequisites.
• Forward or currency-hedging mechanics if the lecturer wants to assess the derivatives component.
• Basic debt and interest-rate concepts.

Delivery format

The platform records team decisions and comparative outcomes. That evidence supports your academic judgement; it does not replace it, and it does not establish which individual student made which argument.

Suggested debrief questions

• What underlying currency risk was the derivatives package trying to change?
• Why did the chosen hedge fit the financing package better than the alternatives?
• What did the CSA change about counterparty exposure and collateral needs?
• Which risk remained after the hedge?
• What market move would make you revise the hedge?

The Portfolio Management Simulation is a 3-6 hour team-based web simulation in which students construct and rebalance an equity portfolio across 25 global companies. It is not a derivatives simulator. Its value in this course is to expose the risk-return problem that futures and options are often used to alter: beta, covariance, volatility, concentration, mandate and rebalancing under new information.

Why it fits here

Use it after students know basic hedging or before the final capstone. The lecturer can ask teams to quantify their portfolio exposure inside the simulation and then, outside the platform, propose an index-futures or protective-put overlay.

What students do

• Work in teams with either a Hedge Fund or Pension Fund mandate.
• Use historical data and a supplied Excel model to estimate expected return, beta, covariance, volatility and portfolio weights.
• Apply CAPM and mean-variance optimisation to construct an initial portfolio.
• Trade and rebalance across four quarters as company and market news arrives.
• Review return, volatility and Sharpe Ratio; Hedge Fund teams also focus on Alpha while Pension Fund teams focus on Sharpe Ratio.

Recommended course placement

• After Session 3 as a risk-exposure lab before an index-futures overlay exercise.
• Before Session 12 as input to the integrated derivatives risk-management capstone.

Knowledge students should have before the simulation

• Basic CAPM.
• Sharpe Ratio.
• Mean-variance optimisation.

Delivery format

The platform records team portfolio choices, trades and comparative outcomes. It does not create individual derivative-trading evidence because students are not trading derivatives in this simulation.

Suggested debrief questions

• Which risk exposure in your final portfolio would you hedge first?
• Would an index future or a put option better fit your mandate, and why?
• How would basis risk affect a futures overlay?
• What percentage of the portfolio would you protect?
• How would the hedge change Alpha, Sharpe Ratio or upside participation?

Grading with the Investment Banking Simulation

The Investment Banking Simulation spans financing, derivatives, advisory and later restructuring across four rounds. For a Derivatives course, grade the connection between the underlying exposure, derivative terms and the broader financing decision rather than the overall leaderboard alone.

• Accuracy and economic logic of the derivatives offer or selection.
• Consistency between currency risk, position, amount and exchange-rate terms.
• Interpretation of Credit Support Annex terms and counterparty implications.
• How derivative and financing packages are compared rather than analysed in isolation.
• Quality of the post-simulation individual reflection or oral defence.

Grading with the Portfolio Management Simulation

The Portfolio Management Simulation does not include derivative trading, so use it as evidence of exposure diagnosis and portfolio-risk judgement. The assessed derivatives work should be an external overlay assignment built from the team's observed portfolio.

• Portfolio risk diagnosis using beta, covariance, volatility and mandate.
• Quality of the proposed index-futures, put or collar overlay outside the platform.
• Hedge ratio, basis-risk and cost assumptions.
• Explanation of how the overlay changes downside, upside participation and risk-adjusted performance.
• Individual defence of why the chosen derivative fits the assigned client mandate.

The platform records team decisions, submitted work and comparative outcomes. That evidence supports your academic judgement; it does not replace it, and it does not establish which individual student made which argument.