volatility-modeling

Name: volatility-modeling
Availability: InStock
Author: joellewis

Model and forecast market volatility using EWMA, GARCH, and implied volatility measures.

164|33|Updated Feb 15, 2026

Search Tags:#risk management #forecasting #volatility #garch #ewma #implied volatility

Ready to run

npx skills add https://github.com/JoelLewis/finance_skills --skill volatility-modeling-joellewis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: volatility-modeling
Source: https://github.com/JoelLewis/finance_skills/tree/main/plugins/wealth-management/skills/volatility-modeling
Command: npx skills add https://github.com/JoelLewis/finance_skills --skill volatility-modeling-joellewis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users understand, model, and forecast market volatility, which is crucial for risk management, options pricing, and trading strategy development.

Core Features & Use Cases

Volatility Modeling: Implement EWMA and GARCH(1,1) models to capture volatility clustering and mean reversion.
Implied Volatility: Extract and interpret implied volatility from option prices.
Volatility Surface Analysis: Understand volatility smiles, skews, and term structures.
Use Case: A portfolio manager needs to assess the risk of a new options strategy. They can use this Skill to analyze the current implied volatility surface and forecast future volatility using GARCH models to determine potential downside risk.

Quick Start

Use the volatility-modeling skill to forecast the next 30 days of volatility using the GARCH(1,1) model with the provided historical return data.

Frequently Asked Questions about volatility-modeling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I forecast market volatility using GARCH models in Python?▼

To forecast market volatility using GARCH models in Python, you need historical return data to estimate parameters. This process captures volatility clustering and mean reversion, generating variance forecasts using numpy and scipy.

What is the difference between EWMA and GARCH for volatility forecasting?▼

EWMA calculates volatility as an exponentially weighted moving average, while GARCH(1,1) models volatility clustering and mean reversion. Both forecast market volatility, but GARCH provides a structured mean-reverting variance prediction.

How do I extract implied volatility from option prices for risk management?▼

Extracting implied volatility from option prices involves interpreting market expectations to inform risk management and trading strategies. This analysis assesses downside risk by comparing implied volatility against GARCH volatility forecasts.

Can I analyze the volatility surface and term structure using numpy and scipy?▼

Yes, you can analyze the volatility surface and term structure using numpy and scipy. This Skill interprets volatility smiles, skews, and term structures to evaluate options pricing and trading strategy risks.

When should I use implied volatility versus historical volatility models for trading?▼

Use implied volatility to interpret market expectations from option prices, and historical volatility models like EWMA or GARCH to forecast future variance. Comparing both identifies mispriced options and assesses portfolio risk.