# Garman-Klass Volatility Calculator MCP for AI Agents AI Agent Connect

> Garman-Klass Volatility Calculator MCP for high-efficiency quantitative finance. It calculates volatility using OHLC price data, offering a more precise metric than standard Parkinson volatility by accounting for intraday price direction. Use it to compare different estimation methods or check if current market turbulence is an outlier compared to historical data.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_HhDeXzHewwxkUE53x5VMfdGnsiRGZyjMQzMiZZQi/ai-agent-connect
- **Tags:** volatility, ohlc, quantitative-finance, garman-klass, risk-management

## Description

Quantitative researchers and risk managers often struggle with volatility estimators that ignore the nuances of intraday movement. This MCP changes that by providing a way to compute the Garman-Klass metric directly through your AI client. Instead of relying on simple close-to-close calculations that miss the intraday story, you can feed your agent Open, High, Low, and Close price arrays to get a much sharper picture of market turbulence. 

Working with these metrics becomes much faster when you connect to the Vinkius catalog, which hosts this tool alongside thousands of others. You can quickly run comparative analyses to see how this method stacks up against Parkinson or Close-to-Close benchmarks. It also helps you spot when things are getting weird; you can determine if the current volatility is a genuine outlier or just standard market noise by checking its position within historical distributions. It turns your agent into a specialized quantitative analyst that can process price arrays and return precise risk metrics in seconds.

## Tools

### calculate_gk_volatility
Computes the primary Garman-Klass volatility metric from OHLC data. It provides a more efficient estimate than Parkinson volatility by including price direction.

### compare_volatility_methods
Runs a comparative analysis between different volatility estimators. Use this to see how Garman-Klass performs against Parkinson or Close-to-Close models.

### get_volatility_percentile
Determines the relative extremity of current volatility. It helps you identify if market turbulence is a statistical outlier.

## Prompt Examples

**Prompt:** 
```
Calculate the Garman-Klass volatility for these prices: Open [100, 102], High [105, 106], Low [98, 101], Close [103, 104].
```

**Response:** 
```
The calculated Garman-Klass volatility is **0.0452**.
```

**Prompt:** 
```
Compare the volatility methods for the following data: Open [10, 11], High [12, 13], Low [9, 10], Close [11, 12].
```

**Response:** 
```
Here is the comparative analysis for your data:

| Method | Volatility |
| :--- | :--- |
| **Garman-Klass** | **0.085** |
| Parkinson | 0.072 |
| Close-to-Close | 0.065 |
```

**Prompt:** 
```
Is a volatility of 0.25 extreme if the historical volatilities were [0.1, 0.15, 0.12, 0.18, 0.2]?
```

**Response:** 
```
Yes, a volatility of **0.25** is considered extreme. It sits in the **100th percentile** relative to your historical data.
```

## Capabilities

### Compute Garman-Klass volatility
Calculate precise volatility metrics using full OHLC price arrays.

### Compare estimation methods
Evaluate the efficiency of different volatility models against each other.

### Identify market outliers
Check if current volatility levels are extreme relative to historical data.

## Use Cases

### Detecting extreme market moves
A risk manager asks their agent if the current volatility is an outlier, and the agent uses `get_volatility_percentile` to confirm it's in the 99th percentile.

### Validating volatility models
A quant researcher uses `compare_volatility_methods` to see if Garman-Klass provides a better signal than Parkinson for a specific asset.

### Automated risk reporting
An analyst provides a week of OHLC data and has the agent calculate the Garman-Klass metric to include in a daily risk summary.

## Benefits

- Get more accurate volatility estimates by using the `calculate_gk_volatility` tool to capture intraday price direction.
- Compare different models instantly with `compare_volatility_methods` to find the best fit for your data.
- Detect market regime shifts using `get_volatility_percentile` to see if current moves are outliers.
- Reduce manual math by letting your agent handle the heavy lifting of OHLC array processing.
- Speed up risk assessment by comparing Garman-Klass against Parkinson and Close-to-Close benchmarks.

## How It Works

The bottom line is you turn raw price data into actionable volatility intelligence through simple natural language commands.

1. Provide your agent with arrays containing Open, High, Low, and Close price data.
2. The agent invokes the calculation tools to process the price arrays.
3. You receive the calculated volatility metric or a comparative analysis report.

## Frequently Asked Questions

**How does the Garman-Klass Volatility Calculator MCP improve my risk analysis?**
It provides a more accurate measure of market turbulence by using intraday price data (OHLC) rather than just closing prices, giving you a better sense of real risk.

**Can I use the Garman-Klass Volatility Calculator MCP to compare different models?**
Yes. You can have your agent compare the Garman-Klass metric against other common estimators like Parkinson or Close-to-Close to see which is most effective for your specific asset.

**How do I know if current market volatility is an outlier using this MCP?**
You can use the percentile tool to compare the current volatility against a provided list of historical values, which tells you exactly how extreme the current movement is.

**What kind of data do I need to provide to this MCP?**
To get the most accurate results, you should provide arrays containing the Open, High, Low, and Close prices for the period you are analyzing.

**Is this MCP compatible with Claude or Cursor?**
Yes, this MCP works with any MCP-compatible client, including Claude, Cursor, and Windsurf.