, refers to an event that has three defining characteristics:

From a quant perspective, Black Swan events expose 

model risk, tail risk, and flawed assumptions

 embedded in traditional financial models.

Most quantitative models are built on assumptions such as:

3. Statistical View: Fat Tails vs Normal Distribution

A “10-sigma event” in a normal distribution should occur once in several billion years — yet markets experience them 

4. Tail Risk and Extreme Value Theory (EVT)

5. Correlation Breakdown & Dependency Risk

Linear correlation (Pearson) fails in tails.

6. Volatility Regimes & Vol-of-Vol Explosion

Overfitted models are most fragile during extremes.

8. Liquidity Risk & Market Microstructure Failure

Liquidity-adjusted risk models become essential.

Since Black Swans cannot be predicted probabilistically:

11. Antifragility: Taleb’s Quant Insight

12. Portfolio Construction Under Black Swan Risk

13. Real-World Black Swan Examples (Markets)