extreme value theory

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Detecting Crashes with Fat-Tail Statistics Detecting Crashes with Fat-Tail Statistics
39 min read Keywords:  fat tails, crash detection, LPPLS, extreme value theory, DFA, Hurst, Taleb, Sornette, Bouchaud, bitcoin, finance, rust, revenue, profit, momentum, volatility targeting

We built fatcrash, a Rust+Python toolkit with 15 crash detection methods: LPPLS, DFA, EVT, Hill, Kappa, Hurst, GSADF, momentum/reversal, price velocity, and more. Tested on 96 drawdowns across BTC, SPY, Gold, 23 forex pairs, and equity crises with honest precision/recall/F1 metrics. Plus: which methods transfer to revenue and profit data.

The Limits of Knowing The Limits of Knowing
10 min read Keywords:  Taleb, fat tails, preasymptotics, errors on errors, extreme value theory, Embrechts, law of large numbers, robustness

Every method in this series rests on one number: how close a system sits to its edge. Nassim Taleb spent a career arguing that this is exactly the number you cannot trust. For fat-tailed systems the data needed to pin down the tail converges too slowly, and being honest about your uncertainty fattens the tail further. This is the counterpunch, and where it leaves us.