# volatility-modeling

Estimates crypto volatility, constructs volatility cones, and forecasts regimes via GARCH and EWMA. Useful for position sizing and stop placement.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 494 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/volatility-modeling/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/agiprolabs-volatility-modeling

## What it does

This skill implements volatility estimators tailored for crypto assets. It covers standard close-to-close deviation, range-based Parkinson, and OHLC-based Garman-Klass and Yang-Zhang methods. For forecasting, it includes RiskMetrics EWMA and GARCH(1,1) models. Annualized calculations rely on 365 trading days rather than standard equity calendars.

The skill outlines steps to build volatility cones across lookback windows to spot whether current levels sit above historical percentiles. It also groups market dynamics into clear volatility regimes, from low-volatility ranges to crisis periods with liquidation cascades. Implementing the reference scripts requires Python with `numpy`, while the ATR stop logic relies on `talib`.

Keep in mind that Garman-Klass assumes zero drift and struggles with opening gaps, which makes Yang-Zhang a safer choice when tokens display sporadic trading gaps.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/volatility-modeling/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/volatility-modeling/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/volatility-modeling/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/volatility-modeling/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills volatility-modeling
```

2. **Manually: put the files in place**
- File: `.github/skills/volatility-modeling/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/volatility-modeling
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/volatility-modeling/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/volatility-modeling/SKILL.md

## When to use it
- select a historical volatility estimator for daily or OHLC price data
- forecast future asset variance using EWMA or GARCH(1,1)
- construct volatility cones to identify historical percentile extremes
- classify market conditions into regimes to adjust position sizing

## Example prompts
- Calculate Garman-Klass volatility for daily BTC OHLC data.
- Construct a volatility cone for ETH across windows of 5, 10, 20, 60, and 120 days.
- Forecast asset variance using the GARCH(1,1) model.

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE 8cae7d4054575941
````text
---
name: volatility-modeling
description: Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones
---

# Volatility Modeling

Volatility — the magnitude of price fluctuations — is arguably the single most
important quantity in trading.  It drives position sizing, stop placement, option
pricing, and regime detection.  This skill covers estimation, forecasting, and
practical application of volatility in crypto markets.

## Why Volatility Matters

| Use Case | How Volatility Is Used |
|---|---|
| **Position sizing** | Scale position inversely with vol so each trade risks a consistent dollar amount |
| **Stop placement** | ATR-based stops widen in high-vol regimes, tighten in low-vol |
| **Strategy selection** | Mean-reversion works in low vol; momentum works in high vol |
| **Risk budgeting** | Vol-target portfolios maintain constant portfolio-level risk |
| **Regime detection** | Vol regime shifts signal changing market dynamics |
| **Option pricing** | Implied vs realized vol gap creates trading opportunities |

## Types of Volatility

### Historical (Realized) Volatility
Computed from observed past returns.  The most common and directly measurable
form.  Multiple estimators exist with different statistical efficiency.

### Implied Volatility
Derived from option prices via Black-Scholes or similar models.  Limited in
crypto DeFi where liquid options markets are sparse, but available on Deribit
for BTC/ETH.

### Forecast Volatility
Predicted future volatility from models like EWMA or GARCH.  Used for
forward-looking position sizing and risk budgets.

---

## Estimation Methods

### 1. Close-to-Close (Standard Deviation of Log Returns)

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

```python
import numpy as np

log_returns = np.log(closes[1:] / closes[:-1])
vol_daily = np.std(log_returns, ddof=1)
vol_annual = vol_daily * np.sqrt(365)  # crypto trades 365 days
```

- **Pros**: Simple, widely understood.
- **Cons**: Uses only close prices — ignores intraday range.

### 2. Parkinson (High-Low Range)

Uses the daily high-low range, which is ~5x more statistically efficient than
close-to-close.

```python
hl_ratio = np.log(highs / lows)
vol_parkinson = np.sqrt(np.mean(hl_ratio**2) / (4 * np.log(2))) * np.sqrt(365)
```

- **Pros**: More efficient, captures intraday moves.
- **Cons**: Downward bias with discrete sampling; ignores close-to-close jumps.

### 3. Garman-Klass (OHLC)

The most efficient single-day OHLC estimator.

```python
hl = np.log(highs / lows)
co = np.log(closes / opens)
````
END THIRD-PARTY FILE 8cae7d4054575941

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