Python simulation of hedging a €1m foreign currency payment with forwards and options, including payoff tables, decision metrics, and visualisation.

• Scenario modelling for a UK company with a €1m exposure due in 3 months.
• Three strategies compared:
  • No hedge → full market exposure
  • Forward hedge → locked rate
  • Call option hedge → downside protection + upside flexibility
• Decision metrics: worst-case cost, best-case cost, forward vs option breakeven rate.
• Automated outputs: payoff table (CSV), comparison chart (PNG), and metrics summary (TXT).

This self-directed project demonstrates how corporates can manage FX risk using forwards and options. I built a Python model to compare the GBP cost of a €1m payment under different EUR/GBP outcomes, showing the trade-offs between certainty, flexibility, and premium cost.

By presenting results as a table, chart, and metrics summary, I mirrored the way a risk desk or sales team would present hedging options to a CFO — balancing theory, coding, and communication.

I wanted to showcase a project beyond trading strategies that still connects directly to real-world market roles. FX hedging sits at the intersection of corporate finance and markets — and it’s exactly the kind of problem a junior in sales & trading or risk management would need to explain to a client.

Unhedged FX exposures create uncertainty: if the euro strengthens, a UK company faces higher GBP costs to settle euro payments. My goals were to:

• Simulate payoffs under multiple EUR/GBP scenarios.
• Show how a forward removes uncertainty but sacrifices upside.
• Show how an option caps downside while keeping upside, but at a cost.
• Highlight breakeven levels where the option beats the forward.

• Forwards: used by corporates for budget certainty.
• Options: bought as “insurance” when firms want protection but flexibility.
• Unhedged: risky but occasionally cheaper.

Banks present this analysis to clients daily; being able to explain it clearly is valuable for roles in markets, treasury, and structuring.

• Defined a base scenario (UK firm, €1m, 3 months).
• Created payoff functions for no hedge, forward, and option.
• Simulated EUR/GBP rates between 0.80–0.95.
• Calculated GBP costs for each strategy.
• Built a payoff table and plotted results.
• Calculated decision metrics (forward cost, option max/min, breakeven).
• Saved results for reproducibility.

• Forward hedge: fixed cost = £1,162,791.
• Option hedge:
  • Max cost = £1,186,320 (strike + premium).
  • Min cost (grid) = £1,076,161 (euro weakens, premium still paid).
  • Breakeven vs forward = EUR/GBP 0.8778.
• Unhedged: ranged between £1,250,000 (euro strengthens) and £1,052,632 (euro weakens).

📊 The chart makes these trade-offs clear:

• Forward = flat line.
• Option = kinked curve with premium.
• No hedge = downward slope (full risk).

• Challenge: Making option logic realistic (exercise vs market).
• Solution: Implemented branch logic + premium cost.
• Challenge: Explaining finance concepts clearly in code.
• Solution: Structured code with comments, payoff tables, and charts.

• Strengthened ability to connect derivative theory with practical modelling.
• Learned how to present financial results in client-ready outputs (tables, charts, summaries).
• Demonstrated both technical coding and financial explanation skills.

Next, I plan to:

• Replace fixed premium with Garman–Kohlhagen option pricing.
• Extend to more complex hedging portfolios (multiple currencies).
• Add Monte Carlo simulation to model distributions of hedging costs.

Decision Metrics

• Forward cost (locked): £1,162,791
• Option max cost: £1,186,320
• Option min cost (grid): £1,076,161
• Breakeven (Option vs Forward): 0.8778 EUR/GBP

Payoff Table (excerpt)

(full CSV in results/payoff_table.csv)