LA:9 Tail Risk – Losses Beyond VaR | Market Risk in Python

Описание: In this lecture, we move beyond Value at Risk (VaR) and focus on tail risk — the behavior of losses in the extreme left tail of the return distribution.

While VaR tells us where losses start to become bad, it says nothing about how severe losses can become once that threshold is crossed. This video shows why that distinction is critical for understanding real-world financial risk.

📌 What this video covers

What tail risk is and how it differs from VaR

Defining the tail as the worst 1% of daily returns

Identifying the tail cutoff using historical VaR

Computing:

Average tail loss

Worst observed daily loss

Number of tail events in the sample

Why average tail losses are much larger than the VaR threshold

Interpreting extreme events (e.g., single-day market drops over 10%)

Visualizing tail losses using a histogram

Why extreme losses are variable and difficult to model

Why VaR alone is insufficient for managing downside risk

How tail risk measures help assess capital impact during crises

Why tail analysis is especially important for:

Leveraged portfolios

Non-linear payoffs

Crisis-driven correlations

This lecture provides the foundation for the next step: stress testing, where we analyze losses under hypothetical market shocks instead of relying only on historical data.

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