Collect implied volatility quotes for relevant strikes and maturities. Use both local and global data sources to provide context. Ensure that the dataset represents typical market conditions, as this will serve as your baseline for scenario analysis. Document data sources and sampling times for transparency and repeatability.

Identify which market variables to shock (such as interest rates, underlying price, or macro events). Base these shocks on plausible historical events or regulatory changes in Australia. Apply the shocks methodically to the volatility surface, keeping detailed notes on all parameter changes for auditability.

Maintain a clear worksheet where each assumption, rationale, and adjustment is logged. This promotes team alignment and facilitates peer review. Include justifications tied to historical events, and highlight any areas where judgement or estimation is required. Review the worksheet with colleagues before proceeding to results analysis.

Execute the scenario analysis using updated model parameters. Compare the resulting volatility surface with historical benchmarks from similar market conditions. Note where your scenario closely tracks past outcomes and where deviations arise. Discuss causes for divergence with your team, and adjust the scenario if necessary to improve relevance and reliability.

Summarise scenario results in a structured report, linking key findings to both model output and historical context. Invite peer review to catch oversights or bias, and update documentation as new insights emerge. This ensures the scenario is robust and ready for use in decision-making or further research.