Learning outcomes
Foundation about 5 min
After this module you can:
- Set up a market-model event study: choose estimation and event windows, estimate normal returns, and compute abnormal returns (AR) and cumulative abnormal returns (CAR).
- Derive the exact finite-sample variance of a CAR, including estimation error, and prove the standardised CAR is -distributed under the null.
- Derive the cross-sectional test statistics (J1, Patell, Boehmer–Musumeci–Poulsen) and show by simulation when each has correct size.
- State how the Caldara–Iacoviello GPR index is built, what its threats and acts sub-indices measure, and what its regressions find.
- Update scenario probabilities on news with Bayes' rule in probability, odds and log-odds form, and compute expected values, the value of information and decision-tree policies.
- Explain why traders hedge geopolitical risk with options, and when a hedge has positive expected value under one's own probabilities.
- Trace a geopolitical shock to Indian assets through oil chokepoints, risk premia, sanctions, trade, capital flows and the rupee.
- Analyse seven episodes (1990, 2022, 2023–24, Apr 2025, May 2025, 2026, Nov 2026) with dated, sourced numbers, and say what each teaches.