volatility-modeling
This skill teaches time-series volatility modeling and options-market approaches to quantify and predict market uncertainty. Learn EWMA and GARCH frameworks for volatility forecasting, understand implied volatility extraction from option prices, and interpret volatility smile, skew, and term structure patterns. Use it to calculate volatility risk premiums, analyze the VIX, and support derivatives pricing and portfolio hedging decisions.
Volatility Modeling helps you forecast market uncertainty using EWMA, GARCH, and implied volatility techniques.
AI-generated summary based on this skill's SKILL.md
Install
JoelLewis/finance_skills/volatility-modeling · repository language: Python
git clone https://github.com/JoelLewis/finance_skills
cp -r finance_skills/plugins/wealth-management/skills/volatility-modeling ~/.claude/skills/volatility-modelingnpx skillfed install JoelLewis/finance_skills/volatility-modelingFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How to calculate EWMA volatility?
volatility-modeling teaches EWMA (Exponentially Weighted Moving Average) as a foundational time-series approach. EWMA assigns exponentially decaying weights to past squared returns, controlled by a decay factor λ (typically 0.94 in RiskMetrics). The current volatility estimate is a weighted blend of the previous estimate and the most recent squared return, making EWMA responsive to recent shocks while smoothing noise. This method is computationally efficient and widely used for real-time risk monitoring.
What is GARCH model for forecasting volatility?
volatility-modeling covers GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) as a parametric framework that models volatility as a function of past squared returns and past conditional variances. GARCH(1,1) is the most common specification, balancing mean reversion with persistence of volatility shocks. Unlike EWMA's fixed decay, GARCH estimates parameters from data, captures volatility clustering, and produces term-structure forecasts. It supports mean-reversion analysis and half-life calculations for shock persistence.
How to extract implied volatility from option prices?
volatility-modeling teaches extraction of implied volatility (IV) by inverting the Black-Scholes formula: given an observed option price, you solve numerically for the volatility input that matches that price. IV reflects the market's expectation of future realized volatility and embeds risk premia. This skill covers the mechanics of IV extraction, interpretation as forward-looking uncertainty, and how IV varies across strikes and maturities to form the volatility smile and skew patterns.
What does volatility smile and skew explain?
volatility-modeling explains that the volatility smile and skew are deviations of implied volatility across option strikes from the flat Black-Scholes assumption. A smile shows IV rising at both out-of-the-money and in-the-money strikes; skew shows IV higher for lower strikes (put skew) or higher strikes (call skew). These patterns reflect market pricing of tail risk, jump risk, and stochastic volatility. Understanding smile and skew dynamics is essential for options pricing, hedging, and volatility surface modeling.
What is the VIX index meaning and how to interpret it?
volatility-modeling covers the VIX as the market's implied volatility index, derived from S&P 500 index option prices across strikes and maturities. VIX measures expected 30-day realized volatility and spikes during market stress, reflecting fear and uncertainty. The skill teaches VIX interpretation as a fear gauge, its role in portfolio hedging, and the volatility risk premium—the tendency for realized volatility to fall short of VIX levels, creating trading opportunities for volatility sellers.
How does volatility-modeling support derivatives pricing and hedging?
volatility-modeling equips you to quantify and predict market uncertainty for derivatives pricing and portfolio hedging. By mastering EWMA and GARCH forecasting, implied volatility extraction, and volatility surface dynamics, you can calibrate option pricing models, estimate Greeks, and construct volatility-aware hedges. The skill integrates realized, implied, and forecasted volatility to support risk management decisions and identify volatility risk premiums for trading strategies.
SKILL.md
rendered from the published skill — quoted content, verbatim
Volatility Modeling
Core Concepts
EWMA (Exponentially Weighted Moving Average)
A simple volatility model that gives more weight to recent observations. RiskMetrics popularized this approach with a standard decay factor.
sigma^2_t = lambda * sigma^2_{t-1} + (1 - lambda) * r^2_{t-1}
where: - lambda = decay factor (RiskMetrics standard: 0.94 for daily, 0.97 for monthly) - r_{t-1} = return in period t-1 (typically demeaned, but for daily returns the mean is often assumed to be zero) - sigma^2_{t-1} = previous period's variance estimate
Properties: - Assigns exponentially decaying weights to past squared
(truncated - see the full file via the links below)
Read as markdown · JSON record · Browse the source repository
File tree — 2 files
plugins/wealth-management/skills/volatility-modeling/SKILL.md
plugins/wealth-management/skills/volatility-modeling/scripts/volatility_modeling.py