Historical Risk

Quantifies how risky an asset or portfolio has actually been using historical data. You get volatility estimators ranging from simple close-to-close to the more sophisticated Yang-Zhang that uses OHLC data, plus drawdown analysis with max drawdown and recovery times, historical VaR pulled straight from the empirical distribution, and downside risk metrics like semi-variance and tracking error. The formulas are all here with worked examples. One thing to watch: these are all backward-looking measures, so you're assuming the past tells you something useful about risk, which works great until the regime changes. Pairs naturally with performance metrics since most risk-adjusted ratios need a volatility or drawdown number in the denominator.

skills.sh installs: 761
GitHub stars: 224
last updated: Aug 21, 2026

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Install

$ npx -y skills add joellewis/finance_skills --skill historical-risk --agent claude-code

Installs into .claude/skills of the current project.

Files

Historical Risk Analysis

Core Concepts

Close-to-Close Volatility

The simplest and most common volatility estimator. Compute the standard deviation of log returns and annualize.

sigma_annual = sigma_daily * sqrt(N)

where N = number of trading periods per year (typically 252 for daily, 52 for weekly, 12 for monthly).

Log returns are preferred: r_t = ln(P_t / P_{t-1}).

Parkinson (High-Low) Estimator

Uses intraday high and low prices to capture intraday volatility that close-to-close misses. More efficient than close-to-close when the true process is continuous.

sigma^2_Park = (1 / (4 * n * ln(2))) * sum( ln(H_i / L_i)^2 )

This estimator is roughly 5x more efficient than close-to-close for a diffusion process, but is biased downward when there are jumps or when the range is discretized.

Yang-Zhang Estimator

Combines overnight (close-to-open), open-to-close, and Rogers-Satchell components. It is unbiased for processes with both drift and opening jumps.

sigma^2_YZ = sigma^2_overnight + k * sigma^2_open-to-close + (1 - k) * sigma^2_RS

where k is chosen to minimize estimator variance:

k = 0.34 / (1.34 + (n + 1) / (n - 1))

SKILL.md on GitHub

Details

Repository: github.com/joellewis/finance_skills
Categories: Debugging, Finance & Trading
First seen: Jun 3, 2026