{"enrichment":{"faq":[{"a":"Volatility Modeling provides GARCH, EWMA, and realized volatility techniques to forecast price swings. GARCH captures volatility clustering\u2014the tendency for large moves to follow large moves. EWMA applies exponential weighting to recent price changes, giving more influence to recent data. Realized volatility aggregates intraday price movements into a direct historical measure. Together, these methods let you predict future market swings across different market conditions.","q":"What methods does volatility-modeling use for GARCH volatility forecasting?"},{"a":"Volatility Modeling enables inverse position sizing: when volatility is high, you reduce position size; when it's low, you can size larger. This keeps your dollar risk constant across market regimes. The skill also covers ATR stop placement strategy, letting you set stops proportionally to current volatility so they're tight in calm markets and wider during turbulent periods, reducing whipsaws.","q":"How does volatility-modeling help with position sizing based on volatility?"},{"a":"Volatility Modeling analyzes volatility cones to determine whether current volatility is historically high or low. A cone plots realized volatility at multiple lookback periods (e.g., 10, 20, 60 days) to show the range of typical values. When current vol sits at the cone's top, you're in a high-volatility regime; at the bottom, a low-volatility regime. This context helps you choose appropriate strategies.","q":"What is volatility cone analysis and how does volatility-modeling use it?"},{"a":"Volatility Modeling classifies market regimes and detects volatility regime shifts to inform strategy selection. By tracking GARCH, EWMA, and realized volatility trends, the skill identifies when the market transitions from calm to turbulent or vice versa. Regime classification lets you switch between mean-reversion strategies (suited to low-vol regimes) and trend-following strategies (suited to high-vol regimes).","q":"How does volatility-modeling classify market regimes and detect vol shifts?"},{"a":"Volatility Modeling addresses crypto's unique volatility profile: annualized volatility typically ranges from 50\u2013150%, far exceeding traditional equities. The skill covers volatility clustering in crypto, tail risk patterns, and crypto market volatility characteristics. These insights help you calibrate position sizes, stop levels, and regime thresholds to crypto's extreme swings rather than applying equity-market assumptions.","q":"What crypto-specific volatility characteristics does volatility-modeling address?"},{"a":"Yes. Volatility Modeling covers daily volatility calculation methods including Parkinson high-low range volatility and Garman-Klass OHLC volatility, which use intraday price ranges for more efficient estimates. The skill also supports volatility percentile historical analysis, letting you rank today's volatility against its past distribution. This percentile context helps you gauge whether current conditions are extreme or typical.","q":"Can volatility-modeling calculate daily volatility and percentile rankings?"}],"shadow_tags":["quantitative-finance","risk-measurement","market-regimes","time-series-modeling","position-management","statistical-estimation","predictive-analytics","portfolio-optimization"],"summary_rewrite":"Volatility Modeling equips traders with methods to measure price fluctuations and predict future market swings using GARCH, EWMA, and realized volatility techniques. The skill covers regime classification, position sizing, and stop placement strategies tailored to crypto's 50\u2013150% annualized volatility levels."},"files":[{"bytes":8367,"path":"skills/volatility-modeling/SKILL.md","sha256":"518aaa379aaa3bc6a9639c7632a438eda07c80ba0ee66b4294d8c78c41afc949","url":"https://skillfed.io/files/agiprolabs/claude-trading-skills/volatility-modeling/662f5800/SKILL.md"}],"id":"agiprolabs/claude-trading-skills/volatility-modeling","links":{"html":"https://skillfed.io/agiprolabs/claude-trading-skills/volatility-modeling","md":"https://skillfed.io/agiprolabs/claude-trading-skills/volatility-modeling.md","repo":"https://github.com/agiprolabs/claude-trading-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":52,"language":"Python","last_updated":"2026-06-24","license":"MIT","name":"volatility-modeling","publisher":"agiprolabs","stars":248},"relations":{"similar":[{"id":"JoelLewis/finance_skills/volatility-modeling"},{"id":"agiprolabs/claude-trading-skills/regime-detection"},{"id":"HKUDS/Vibe-Trading/quant-statistics"},{"id":"terrylica/cc-skills/garch-vol-recipes"},{"id":"agiprolabs/claude-trading-skills/mean-reversion"},{"id":"JoelLewis/finance_skills/historical-risk"},{"id":"HKUDS/Vibe-Trading/correlation-analysis"},{"id":"anthropics/financial-services/option-vol-analysis"},{"id":"agiprolabs/claude-trading-skills/correlation-analysis"},{"id":"HKUDS/Vibe-Trading/hedging-strategy"}]},"slug":{"owner":"agiprolabs","repo":"claude-trading-skills","skill":"volatility-modeling"},"version":"662f5800"}