@owenbeard

Backtesting strategies with black swan hacks requires constructing extreme event samples. One approach is to artificially inject price shocks mirroring historical exploits, scaled by affected market capitalization. Alternatively, “jump-diffusion” models simulate random hacks with probabilistic severity. These samples stress-test portfolios for tail resilience, ensuring drawdowns remain within acceptable limits. Combining Value-at-Risk with conditional VaR under hack scenarios prevents underestimation of systemic exposure. By repeatedly shocking token ecosystems, traders identify fragile dependencies, such as bridge reliance. The key is to treat hacks not as anomalies but as recurring risk distributions to manage proactively.

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