Investor ArchetypeResponses to Black Swan EventSeverity: A Monte-Carlo Simulation Study

Black Swan eventsrare but severe market shockspose significant challenges to traditional risk management frameworks, which often rely on average-case assumptions and fail to capture tail risk dynamics. This paper investigates how five types of investor behavior perform under such extreme conditions using a Monte Carlo jump-diffusion simulation framework. The model generates terminal wealth distributions across six stylized market scenarios, each representing different magnitudes of positive and negative price jumps. The investor heuristics studied include rational (mean-variance), loss-averse, momentum-based, contrarian, and leveraged strategies. Results reveal pronounced differences in tail risk exposure: loss-averse investors achieve the most robust downside protection, while leveraged and momentum strategies exhibit substantial vulnerability to large negative shocks. Contrarian and rational types fall between these extremes in both risk and reward potential. By comparing not only median outcomes but also the 5th and 95th percentiles, the analysis highlights trade-offs between downside containment and upside participation. These findings offer practical implications for behavioral modeling, stress-testing frameworks, and regulatory design aimed at improving portfolio resilience under low-probability, high-impact conditions.