Research preview

The sudden escalation of attacks on Ukraine’s energy infrastructure after a high‑profile political statement highlights how quickly geopolitical events can reshape market dynamics. For quantitative traders, the lesson is clear: models that performed well under stable conditions can break down when the underlying risk landscape shifts dramatically. This article examines the statistical pitfalls of over‑fitting, illustrates why out‑of‑sample testing is essential, and offers concrete steps to safeguard trading systems against sudden energy‑related market turbulence. The danger of fitting noise in high‑dimensional models A common temptation in quantitative research is to throw many predictors into a regression or machine‑learning model in hopes of capturing every nuance of market behavior....