How to build a risk management course: a complete guide for lecturers

Central question: Which risks are material, who bears them, and when is taking risk economically justified?

Start by giving students a common language for risk rather than a list of formulas. The teaching aim is to distinguish uncertainty, exposure, loss and volatility, then connect risk taking to expected return, strategic purpose and capacity to absorb loss.

What this concept covers

• Risk, uncertainty, exposure, hazard and loss.
• Financial and non-financial risk taxonomies.
• Inherent, residual and emerging risk.
• Risk-return trade-offs, risk capacity and risk tolerance.
• Risk ownership and the difference between identifying a risk and deciding what to do about it.

Learning outcomes

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

• Classify material risks in a business or portfolio.
• Distinguish risk measures from risk decisions.
• Explain why eliminating all risk is neither possible nor desirable.
• Separate risk appetite, tolerance, capacity and limits.

How to teach it

• Open with one company and ask groups to build competing risk taxonomies.
• Use a heat map only as a starting point, then require evidence and financial consequences.
• Ask students to distinguish risks that should be avoided, reduced, transferred or accepted.

Case-style example

Meridian Mobility earns EUR250 million of revenue at a 12% EBITDA margin. Forty percent of sales are in US dollars, its largest customer is 22% of revenue, and a battery supplier provides 60% of a critical component. Students rank the three exposures, estimate which could threaten more than EUR10 million of EBITDA, and recommend one immediate control. One group may prioritise currency volatility while another prioritises supplier concentration. Students discover that materiality depends on both likelihood and loss-bearing capacity.

Where students struggle

Students often produce long risk registers without prioritising materiality. Push them to state what could be lost, over what horizon, and what decision changes as a result.

Reading and a quick check

Use Hull, Chapter 1, then ask students to rewrite a vague risk statement into exposure, trigger, consequence and owner.

Applied teaching activity

Use the case-style example as a seminar decision, risk-committee role-play or short memo. The purpose is to make students prioritise evidence and action rather than add an unrelated simulation.

Where it leads

The next step is to turn a risk taxonomy into governance: who sets appetite, who monitors limits and who has authority to escalate.

Central question: How should an organisation translate board-level risk appetite into decisions that managers can actually follow?

Governance gives risk management authority and discipline. Students should see appetite statements, delegated limits, three-lines responsibilities, escalation triggers and committee challenge as a connected operating system rather than separate compliance documents.

What this concept covers

• Board and senior-management accountability.
• Risk appetite, tolerance, capacity, limits and key risk indicators.
• Risk ownership, challenge and escalation.
• Three-lines responsibilities and committee structures.
• Culture, incentives and the danger of limits that are technically met but economically misleading.

Learning outcomes

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

• Draft a decision-useful risk appetite statement.
• Convert appetite into measurable limits and escalation triggers.
• Allocate ownership and challenge responsibilities.
• Evaluate whether incentives are consistent with stated appetite.

How to teach it

• Compare a vague appetite statement with one tied to earnings, liquidity and capital.
• Give groups the same risk event but different committee roles.
• Require an escalation pathway with a named decision right at each threshold.

Case-style example

Halcyon Bank states that annual trading losses should not exceed 8% of pre-tax earnings. A desk is within its EUR25 million VaR limit, yet its stress loss has risen from EUR35 million to EUR70 million after correlations tightened. Students decide whether the desk is still within appetite, set an escalation trigger and identify who can reduce the limit. One conclusion favours formal limit compliance; another favours stress evidence. Students discover why governance must address conditions that a single metric misses.

Where students struggle

Students commonly treat appetite, tolerance and limits as synonyms. Require them to show the hierarchy from strategic willingness to take risk down to measurable operating boundaries.

Reading and a quick check

Use Hull, Chapter 24, and ask students to turn one qualitative appetite sentence into two measurable limits and one escalation rule.

Applied teaching activity

Use the case-style example as a seminar decision, risk-committee role-play or short memo. The purpose is to make students prioritise evidence and action rather than add an unrelated simulation.

Where it leads

Once governance is clear, students need quantitative tools for expressing uncertainty and comparing exposures.

Central question: How much could be lost, with what confidence, and what remains invisible when a risk number is compressed into one statistic?

This concept should build quantitative literacy without turning the course into a statistics module. Teach Value at Risk and Expected Shortfall as decision aids whose usefulness depends on horizon, distributional assumptions, data windows and the behaviour of correlations in stress.

What this concept covers

• Return and loss distributions, volatility and correlation.
• Confidence levels and time horizons.
• Value at Risk and Expected Shortfall.
• Historical, parametric and model-based approaches.
• Backtesting, tail risk and model limitations.

Learning outcomes

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

• Interpret VaR and Expected Shortfall in plain language.
• Compare alternative measurement approaches.
• Identify assumptions that can understate tail risk.
• Explain why a risk measure should be paired with stress testing.

How to teach it

• Calculate a small historical VaR example before introducing software.
• Change the lookback window and compare the result.
• Ask students what a 99% VaR does not say about the 1% tail.

Case-style example

Northstar Fund holds a EUR100 million trading portfolio. Its one-day 99% VaR is EUR2.4 million and Expected Shortfall is EUR4.1 million. After a volatility shock, the same positions produce VaR of EUR3.1 million and Expected Shortfall of EUR6.8 million. Students compute the percentage increase, explain why tail loss rose faster, and advise whether a EUR3.5 million daily loss limit remains credible. Students discover that identical positions can look very different when the distribution changes.

Where students struggle

Students often describe VaR as the maximum possible loss. Mark down that wording. It is a quantile-based threshold, not a worst-case bound.

Reading and a quick check

Use Hull, Chapters 11-13, and finish with a one-sentence interpretation of a 10-day 99% VaR and a separate sentence on what it omits.

Applied teaching activity

Use the case-style example as a seminar decision, risk-committee role-play or short memo. The purpose is to make students prioritise evidence and action rather than add an unrelated simulation.

Where it leads

With measurement foundations in place, students can examine how diversification and concentration shape portfolio risk.

Central question: When does diversification reduce risk, and when do common factors make apparently different positions move together?

Portfolio risk is where correlation becomes economically meaningful. Students should move beyond counting holdings and instead examine covariance, concentration, beta, mandate constraints and risk-adjusted performance.

What this concept covers

• Diversification and covariance.
• Systematic and idiosyncratic risk.
• Beta, factor exposure and concentration.
• Mean-variance logic and risk-adjusted performance.
• Rebalancing under changing information and client mandates.

Learning outcomes

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

• Evaluate whether a portfolio is genuinely diversified.
• Identify concentrations that are hidden by security count.
• Compare portfolio choices using return, volatility, Alpha and Sharpe Ratio.
• Defend a rebalance that fits a stated mandate.

How to teach it

• Give groups two portfolios with the same number of holdings but different correlations.
• Require a mandate-first recommendation before showing performance.
• Debrief why a hedge-fund objective and a pension-fund objective can justify different choices.

Case-style example

Atlas Pension has EUR500 million invested 60% in global equities, 30% in bonds and 10% in cash. A technology factor accounts for 38% of total portfolio variance despite only 24% of capital. Students reduce factor concentration while keeping expected return above 6.5% and annual volatility below 10%. One group may cut technology; another may add diversifying assets. Students discover that portfolio risk is driven by covariance and mandate constraints, not simply by headline weights.

Where students struggle

Students frequently equate more holdings with more diversification. Make them identify common factor exposures and explain what happens when correlations rise in stress.

Reading and a quick check

Use Hull, Chapter 4 plus the portfolio-risk material in Chapters 8-9. Ask students to identify one concentration that is visible in weights and one that is visible only in covariance.

Where a simulation fits

The Portfolio Management Simulation fits directly after students know CAPM, Sharpe Ratio and mean-variance optimisation. Teams manage a global equity portfolio under either a Hedge Fund or Pension Fund mandate and must connect diversification, trades and risk-adjusted outcomes to the client objective.

Where it leads

Portfolio risk leads naturally into the specific market variables that move asset values and funding costs.

Central question: Which market variables can materially change value or cash flow, and how should exposure be measured and managed?

Market risk becomes teachable when students connect a price move to an economic exposure. Cover interest-rate, foreign-exchange, equity and volatility risk through sensitivity, duration, scenario analysis and selective hedging rather than through derivatives mechanics alone.

What this concept covers

• Interest-rate and yield-curve risk.
• Foreign-exchange transaction and economic exposure.
• Equity-price and volatility risk.
• Duration, sensitivity and basis risk.
• Hedging objectives, residual risk and hedge effectiveness.

Learning outcomes

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

• Map market variables to earnings, cash flow and value.
• Estimate first-order sensitivity to a market move.
• Evaluate whether a hedge reduces the risk that actually matters.
• Explain residual, basis and liquidity risks after hedging.

How to teach it

• Use a corporate exposure alongside a securities portfolio so students see both earnings and mark-to-market effects.
• Ask for the risk objective before allowing any hedge recommendation.
• Run a scenario where the hedge works mechanically but creates liquidity pressure.

Case-style example

Veridian Exporters invoices USD120 million a year while 70% of its costs are in euros. The current EUR/USD rate is 1.10 and management fears a 10% dollar fall. A six-month forward removes most cash-flow uncertainty but locks out favourable upside; options cost EUR1.4 million. Students estimate the unhedged revenue effect and recommend a hedge ratio. Two defensible answers emerge depending on appetite for earnings volatility. Students discover that hedge choice is a policy decision, not only a pricing exercise.

Where students struggle

Students can become absorbed in instrument pricing and lose the underlying exposure. Ask first: what cash flow or value are we trying to protect, over what horizon, and against which move?

Reading and a quick check

Use Hull, Chapters 5, 8, 14 and 15 selectively. Quick check: ask students to distinguish price risk, basis risk and liquidity risk in one hedge.

Where a simulation fits

The Portfolio Management Simulation provides a practical market-risk setting because teams rebalance as prices and news change, then compare return, volatility and mandate-specific risk-adjusted performance.

Where it leads

The next concept shifts from market prices to the possibility that a borrower or counterparty cannot meet its obligations.

Central question: How likely is a borrower or counterparty to fail, how much would be lost, and which protections actually improve recovery?

Credit risk should connect borrower economics to financing terms. Teach probability of default, exposure, loss given default, concentration, covenants, collateral and recovery alongside judgement about business quality and downside cash flow.

What this concept covers

• Probability of default, exposure at default and loss given default.
• Borrower leverage, coverage and cash-flow resilience.
• Counterparty and wrong-way risk.
• Concentration by borrower, sector and correlated exposure.
• Covenants, security, seniority, guarantees and recovery.

Learning outcomes

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

• Assess a borrower using cash flow and leverage evidence.
• Explain how covenant and security choices change lender protection.
• Identify concentration and counterparty risks.
• Recommend approve, reprice, restructure or decline with reasons.

How to teach it

• Give lender and borrower teams the same company but different objectives.
• Require a downside case before a pricing recommendation.
• Make students explain which term protects against which risk.

Case-style example

Oakline Services produces EUR25 million of EBITDA and seeks EUR100 million of debt at 7.5%. Base-case interest coverage is 2.6x, but a 20% EBITDA decline reduces it to 2.1x and raises leverage from 4.0x to 5.0x. Students decide whether to approve, reprice or reduce the facility and choose one covenant plus one security protection. Borrower and lender answers should differ. Students discover that price alone cannot compensate for an unsustainable structure.

Where students struggle

Students often collapse credit quality into a single rating or leverage multiple. Require them to connect default likelihood, exposure, recovery and concentration separately.

Reading and a quick check

Use Hull, Chapters 17 and 19. Quick check: have students explain why two loans with the same PD can have different expected losses.

Where a simulation fits

The Debt Financing Simulation fits once students can analyse borrower capacity. Paired lender and borrower teams negotiate amount, interest, maturity, repayment, security, guarantees, seniority, covenants and other protections for a refinancing decision.

Where it leads

Credit quality can deteriorate quickly when liquidity is weak, so the course next turns to cash conversion, funding and contingency capacity.

Central question: Can the organisation meet cash obligations when they fall due without destroying value through forced actions?

Liquidity risk is different from profitability and from solvency. Students should examine cash conversion, funding sources, collateral calls, contingency buffers and the speed at which assets or working-capital positions can be converted into cash.

What this concept covers

• Market liquidity and funding liquidity.
• Cash conversion cycle, DSO, DIO and DPO.
• Liquidity buffers and contingency funding.
• Collateral and margin calls.
• Early-warning indicators, cash forecasting and stakeholder trade-offs.

Learning outcomes

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

• Distinguish liquidity pressure from solvency weakness.
• Calculate working-capital metrics and identify cash trapped in operations.
• Evaluate contingency funding options.
• Recommend liquidity actions while considering customer, supplier and operational consequences.

How to teach it

• Pair a profitable company with a cash shortfall to expose the distinction.
• Use a 13-week cash-flow view or simplified monthly forecast.
• Ask which actions create cash fastest and which create hidden commercial costs.

Case-style example

Crestline Retail reports EUR18 million of EBITDA but only EUR3 million of cash. DSO is 62 days, DIO 95 days and DPO 38 days. A new store rollout requires EUR8 million within 60 days. Students calculate the cash conversion cycle, select two operational levers and estimate whether a 10-day DSO reduction plus a 15-day inventory reduction closes the gap. One group may protect supplier terms; another may slow expansion. Students discover that profitable growth can still fail through liquidity timing.

Where students struggle

Students may call every cash problem a solvency problem. Force them to ask whether value is insufficient or simply unavailable at the required time.

Reading and a quick check

Use Hull, Chapter 21, and pair it with a short working-capital exercise. Ask students to name one liquidity action that improves cash but may weaken another stakeholder relationship.

Where a simulation fits

The Working Capital Management Simulation fits here as an optional individual application. Students act as CFO, calculate DSO, DIO, DPO and the cash conversion cycle, make receivables, inventory and payables decisions across 12 simulated months, then recommend whether expansion should proceed.

Where it leads

Liquidity pressure becomes more dangerous when leverage is high. The next concept examines capital structure and distress risk.

Central question: How much financial risk can the business absorb before leverage turns from a financing tool into a constraint on survival?

Risk Management students need to read a capital structure from both borrower and creditor perspectives. The focus is debt capacity, interest burden, covenant headroom, refinancing, recovery priorities and what changes when the original financing plan breaks.

What this concept covers

• Debt capacity and leverage.
• Interest coverage, maturity profile and refinancing risk.
• Covenants, security and seniority.
• Financial distress, claim priority and recovery.
• Restructuring choices, haircuts, waivers and debt-for-equity outcomes.

Learning outcomes

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

• Evaluate sustainable leverage under base and downside cases.
• Identify refinancing and covenant risks.
• Map stakeholder claims and bargaining positions in distress.
• Recommend financing or restructuring actions that preserve value where possible.

How to teach it

• Build the base financing case, then remove 20% of EBITDA and shorten refinancing time.
• Have lender and equity groups value the same downside differently.
• Use claim priority to explain why nominal exposure and economic recovery diverge.

Case-style example

Ardent Software has EUR40 million of EBITDA, EUR180 million of debt at an 8% cash interest rate and a 5.0x leverage covenant. A customer loss could reduce EBITDA by 20% while EUR90 million matures in 14 months. Students calculate leverage and interest coverage before and after the shock, then choose between refinancing, equity injection or early restructuring. Equity may prefer waiting while lenders prefer de-risking. Students discover that timing and claim priority shape risk appetite in distress.

Where students struggle

Students often celebrate leverage in return models but underweight refinancing, covenant and recovery risk. Require every leverage recommendation to survive a downside case.

Reading and a quick check

Use Hull, Chapters 2 and 19 selectively, then ask students to state the first assumption that makes the capital structure unsafe.

Where a simulation fits

The Debt Financing Simulation fits before distress, when financing terms are still negotiable. The Debt Restructuring Simulation fits after students understand seniority and recovery, placing Lien 1, Lien 2 and Equity teams into an out-of-court restructuring where claims exceed enterprise value.

Where it leads

Not all severe losses begin in markets or balance sheets. Operational failures can interrupt the processes on which every financial control depends.

Central question: How should a firm prevent, absorb and recover from failures in people, processes, systems and external dependencies?

Operational risk should be taught as loss and resilience, not as a catalogue of incidents. Students need to link controls to failure modes, identify critical services, assess third-party concentration and decide what level of disruption is tolerable.

What this concept covers

• Process, people, systems and external-event risk.
• Cybersecurity and data compromise.
• Third-party and cloud concentration.
• Business continuity, incident response and recovery objectives.
• Control design, key risk indicators and lessons from near misses.

Learning outcomes

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

• Map an operational loss event to failed controls.
• Identify critical services and single points of failure.
• Evaluate preventive, detective and recovery controls.
• Design escalation and post-incident learning actions.

How to teach it

• Use an incident timeline and reveal facts in stages.
• Ask groups to identify the control that should have prevented, detected and contained the event.
• Require a recovery objective rather than a generic recommendation to improve cybersecurity.

Case-style example

CloudPort Payments suffers a six-hour outage that delays EUR140 million of transactions and triggers EUR2 million of client compensation. Eighty percent of processing depends on one cloud region and the tested recovery target is four hours. Students identify the failed preventive and recovery controls, estimate whether current tolerance was breached, and choose between multi-region architecture or enhanced recovery testing. Students discover that resilience decisions trade cost against interruption tolerance rather than promising zero failure.

Where students struggle

Students tend to list more controls without judging whether they address the actual failure mode. Ask what risk each control changes and how success would be observed.

Reading and a quick check

Assign the 2021 Basel operational-risk principles and NIST Cybersecurity Framework 2.0. Quick check: separate prevention, detection, response and recovery controls for one incident.

Applied teaching activity

Use the case-style example as a seminar decision, risk-committee role-play or short memo. The purpose is to make students prioritise evidence and action rather than add an unrelated simulation.

Where it leads

Operational and financial risks become most revealing when multiple shocks are combined, which is the purpose of stress testing.

Central question: What combination of shocks would make the current strategy, portfolio or financing structure unacceptable?

Stress testing should not be taught as a forecast. It is a disciplined way to expose fragility, challenge assumptions, identify nonlinear interactions and define management action before a crisis. Reverse stress testing starts with failure and works backward to plausible causes.

What this concept covers

• Sensitivity analysis versus scenario analysis.
• Historical and hypothetical stress scenarios.
• Multi-factor and correlated shocks.
• Reverse stress testing and break points.
• Management actions, second-order effects and escalation.

Learning outcomes

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

• Design a coherent multi-factor stress scenario.
• Identify break points for earnings, liquidity, capital or covenants.
• Distinguish a severe-but-plausible scenario from a forecast.
• Recommend pre-agreed management actions and escalation triggers.

How to teach it

• Build one scenario collaboratively and force explicit dependencies between shocks.
• Compare management actions before and after a breach threshold.
• Use reverse stress to expose assumptions that ordinary sensitivity tables miss.

Case-style example

DeltaBank holds EUR6 billion of fixed-rate securities funded partly by rate-sensitive deposits. Students test a scenario with rates up 250 basis points, 15% deposit outflows and a 25% rise in corporate credit losses. The task is to identify the first binding constraint and specify two management actions. A treasury team may prioritise liquidity while a credit team prioritises capital preservation. Students discover that the most dangerous outcome can come from interacting risks rather than the largest single shock.

Where students struggle

Students frequently create scenarios that are severe but internally inconsistent. Require a causal story, a time horizon and explicit management responses.

Reading and a quick check

Use Hull, Chapters 10 and 16. Quick check: ask students to write one reverse-stress statement beginning, "The business becomes non-viable if..."

Where a simulation fits

The Debt Restructuring Simulation can follow a stress-testing session as a downside application. Students see how a broken capital structure changes bargaining power, recoveries and feasible management actions once the stress has become real.

Where it leads

Stress tests depend on models and data. The next concept asks when those tools themselves become a source of risk.

Central question: When should a decision-maker trust a model, and what governance is needed when models or AI shape high-impact decisions?

Model risk sits at the intersection of quantitative methods, data, incentives and governance. Students should learn model inventory, validation, limitations, monitoring, override rules and the added risks introduced by machine-learning and generative-AI systems.

What this concept covers

• Model definition, inventory, materiality and ownership.
• Independent validation and challenger models.
• Data quality, drift and monitoring.
• Explainability, bias, human override and AI governance.
• Use limitations, change control and retirement.

Learning outcomes

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

• Identify material model-risk sources.
• Design validation tests proportionate to model use.
• Evaluate data and drift evidence before deployment.
• Recommend governance for human override, monitoring and escalation.

How to teach it

• Give students a high-performing model with weak governance evidence.
• Separate predictive accuracy from fitness for decision use.
• Ask what would trigger recalibration, restriction or withdrawal.

Case-style example

Prism Lending replaces a logistic credit model with an AI model whose validation AUC rises from 0.72 to 0.81. Yet approval rates for one customer segment fall 9 percentage points and default calibration deteriorates after three months. Students choose deploy, restrict or pause, specify two validation tests and set a monitoring trigger. One group may value predictive lift while another prioritises stability and fairness. Students discover that better average accuracy does not automatically make a model safer to use.

Where students struggle

Students can treat model risk as coding error. Emphasise that a mathematically correct model can still be mis-specified, poorly governed, fed weak data or used outside its intended purpose.

Reading and a quick check

Use Hull, Chapter 22, plus NIST AI RMF 1.0. Quick check: require one model limitation, one monitoring metric and one human-override rule.

Applied teaching activity

Use the case-style example as a seminar decision, risk-committee role-play or short memo. The purpose is to make students prioritise evidence and action rather than add an unrelated simulation.

Where it leads

The course closes by integrating emerging, long-horizon and stakeholder risks into enterprise-level decisions rather than treating them as side topics.

Central question: How should an organisation integrate climate, ESG and strategic risks with financial risk without turning the risk framework into a disconnected reporting exercise?

The final concept brings together financial and non-financial risks through enterprise risk management. Climate and ESG are useful because they create long horizons, uncertain pathways, stakeholder conflict and transmission channels into cash flow, asset values, financing and reputation.

What this concept covers

• Enterprise risk aggregation and interdependencies.
• Physical and transition climate risk.
• ESG, stakeholder and conduct risks.
• Risk-adjusted strategic decisions and capital allocation.
• Board reporting, ownership, disclosure and escalation.

Learning outcomes

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

• Trace non-financial risks into financial outcomes.
• Compare short- and long-horizon risk evidence.
• Integrate stakeholder constraints into a risk decision.
• Defend an enterprise-level recommendation using multiple risk categories.

How to teach it

• Map a climate or ESG issue through operational, market, credit and reputational channels.
• Make teams choose between actions that improve different stakeholder outcomes.
• Close with a risk committee recommendation rather than a descriptive ESG report.

Case-style example

GreenForge Steel generates EUR60 million of EBITDA. A new carbon-cost scenario could reduce EBITDA by EUR12 million by 2030 unless the company invests EUR30 million in cleaner equipment. The union seeks a 6% wage rise and lenders offer a 40-basis-point pricing reduction if emissions targets are met. Students compare invest-now, phase-in and defer strategies across cash flow, credit, workforce and transition risk. Students discover that ESG becomes decision-useful when it is translated into financial exposures, constraints and governance choices.

Where students struggle

Students may treat ESG as a separate sustainability chapter. Require them to show the transmission mechanism into cash flow, funding, operations, stakeholder behaviour or strategic optionality.

Reading and a quick check

Use Hull, Chapters 23-24, the Basel climate-risk principles and Giglio, Kelly and Stroebel on climate finance. Quick check: trace one climate driver through at least two financial-risk channels.

Where a simulation fits

The ESG Simulation fits here as an integrative stakeholder exercise. Management, Investor, Regulator and Union teams analyse evidence and negotiate operational, financial and workforce terms while preserving a minimum EBITDA constraint and agreeing an ESG Policy Statement.

Where it leads

This final concept positions students to assemble an integrated risk committee pack and defend which risks deserve action, capital and escalation.

10-week course structure

A 10-session Risk Management course should compress adjacent topics without dropping credit, liquidity, stress or model-risk thinking. The safest compression combines foundations with governance and embeds application inside the quantitative and financing sessions.

14-week course structure

A 14-session course creates space for a fuller measurement sequence, a dedicated market-risk session, separate simulation debriefs and deeper model and operational-risk work. Use the extra time for challenge and reflection rather than simply adding more regulation.

The Portfolio Management Simulation places teams into an active investment mandate across 25 global companies. Students use a downloadable Excel model with market data, expected returns, beta, covariance and volatility, then construct and rebalance a portfolio through changing quarterly information. Hedge Fund and Pension Fund mandates create different definitions of a good outcome, so the same market evidence can support different risk decisions.

Why it fits here

Students already know diversification, covariance, beta, risk-adjusted performance and the distinction between a risk metric and a mandate. The simulation therefore turns portfolio theory into a sequence of decisions rather than introducing those ideas for the first time.

What students do

• Review the client mandate and identify whether Alpha or Sharpe Ratio should dominate the risk objective.
• Use the model and company data to set initial portfolio weights across the available securities.
• Buy, sell and rebalance as market information and company news change over four decision periods.
• Track return, volatility, concentration and mandate-specific risk-adjusted performance.
• Compare the final portfolio with same-role teams and explain which trades improved or weakened the risk decision.

Recommended course placement

• At the end of Session 5 after portfolio and market-risk teaching.
• Across Sessions 4-5 where the lecturer wants preparation, live decisions and a separate debrief.
• Before an individual portfolio-risk memo so students must translate activity data into a defended recommendation.

Knowledge students should have before the simulation

• Basic CAPM and beta.
• Sharpe Ratio and risk-adjusted return.
• Mean-variance optimisation and covariance.
• Diversification, concentration and the difference between mandate objectives.

Delivery format

Suggested debrief questions

• Which concentration mattered more than its portfolio weight suggested?
• When did your mandate cause you to reject a trade with attractive expected return?
• Which change in market information most altered your risk view?
• Did your best-performing trade also improve risk-adjusted performance?
• Which model input or assumption would you challenge before repeating the decision?
• How would your portfolio change if correlations rose sharply in a stress period?

The Debt Financing Simulation puts students into the refinancing of Softie, a private-equity-backed software company. Borrower and lender teams analyse the same company and industry evidence but approach the transaction with different risk appetites. They must negotiate a complete financing package rather than only an interest rate, which makes credit risk, covenant design, security, repayment and bargaining trade-offs visible.

Why it fits here

By Session 8, students can evaluate borrower cash flow, leverage, coverage, downside resilience and credit protection. The simulation therefore tests whether they can convert a risk view into terms and defend why those terms are proportionate.

What students do

• Analyse Softie and relevant industry comparables from the perspective of either lender or borrower.
• Form a financing position on amount, interest, maturity and repayment structure.
• Negotiate security, guarantees, seniority, covenants and other protections rather than treating pricing as the only risk lever.
• Respond to the counterparty and decide which terms are essential, negotiable or worth trading away.
• Submit the agreed financing package or final position, then compare outcomes across independent negotiation pairs.

Recommended course placement

• After Session 8 once debt capacity, credit risk and covenant logic have been taught.
• Across two shorter teaching blocks where teams prepare separately before the negotiation.
• Before an individual credit or financing-risk memo, with the memo explaining which negotiated term mattered most in the downside case.

Knowledge students should have before the simulation

• Basic debt and equity concepts.
• Leverage, interest coverage and debt capacity.
• Covenants, security, guarantees and seniority.
• The difference between borrower flexibility and lender downside protection.

Delivery format

Suggested debrief questions

• Which term best protected the lender in the downside case?
• Which borrower flexibility was most valuable and why?
• Did the parties price risk or change the structure of risk?
• Which covenant would trigger first if operating performance weakened?
• What information would have changed your walk-away point?
• How would the negotiated package affect refinancing risk at maturity?

The platform provides decision evidence, not automatic academic grades. Build the rubric around course outcomes, then use recorded choices, terms, calculations and comparative outcomes as supporting evidence.

Grading with the Portfolio Management Simulation

Students construct and rebalance a portfolio under either a Hedge Fund or Pension Fund mandate. The assessment should reward mandate fit and risk reasoning, not simply the highest raw return.

• Quality of portfolio construction and diversification rationale.
• Use and critique of expected return, beta, covariance, volatility and portfolio weights.
• Rebalancing logic in response to new information.
• Interpretation of Alpha or Sharpe Ratio in the relevant mandate.
• Individual memo explaining one decision the team would change and why.

Grading with the Debt Financing Simulation

Borrower and lender teams negotiate a complete refinancing package for Softie. Grade the coherence of the package and the risk logic behind the negotiated trade-offs.

• Debt-capacity and downside judgement.
• Interest, maturity and repayment logic.
• Covenant, security, guarantee and seniority rationale.
• Negotiation discipline and ability to identify walk-away terms.
• Individual defence of the term that most changes downside risk.

Grading with the Debt Restructuring Simulation

Lien 1, Lien 2 and Equity teams negotiate an out-of-court restructuring when claims exceed enterprise value. The purpose is to test priority, recovery and stakeholder judgement under distress.

• Accuracy of claim and recovery reasoning.
• Recognition of seniority and bargaining power.
• Quality of concessions and value-preserving agreement logic.
• Ability to distinguish nominal claim from expected recovery.
• Individual note on the alternative if negotiation fails.

Grading with the Working Capital Management Simulation

Students act individually as CFO across 12 simulated months, so the activity naturally produces attributable evidence alongside calculations and policy choices.

• Accuracy of DSO, DIO, DPO and cash conversion calculations.
• Quality of receivables, inventory and payables decisions.
• Balance between liquidity, profitability, customer and supplier consequences.
• Use of evidence in the final expansion recommendation.
• Consistency between monthly choices and final rationale.

Grading with the ESG Simulation

Management, Investor, Regulator and Union teams negotiate an ESG policy and related financial, operational and workforce terms while preserving a minimum EBITDA constraint.

• Use of role-specific evidence rather than generic ESG language.
• Financial and operational consistency of proposed terms.
• Recognition of stakeholder trade-offs and constraints.
• Quality of the final shared ESG Policy Statement.
• Individual reflection on which risk was transferred, reduced, retained or created.

Evidence limit: The platform records what each team decided, the terms they agreed and comparative outcomes across groups. That evidence supports your academic judgement; it does not replace it, and it does not establish which individual student made which argument.