{"enrichment":{"faq":[{"a":"garch-volatility-toolkit builds volatility forecasts using GARCH(1,1) and GJR(1,1) models with walk-forward fitting, then applies them to scale positions inversely to risk. The toolkit includes tested recipes for univariate fits, DCC correlation overlays, and position-sizing logic on BTC, ETH, SOL, and AVAX futures data. Results are cost-dependent: GJR vol-scaling works at institutional rates (2bps) but erodes at retail spreads (7bps); DCC de-weighting adds minimal economic value.","q":"What is garch-volatility-toolkit and what does it do?"},{"a":"garch-volatility-toolkit enables you to forecast asset volatility using GARCH(1,1) and GJR(1,1) models for dynamic position sizing. Fit models in walk-forward windows without lookahead bias, then scale position sizes inversely to predicted volatility. The toolkit provides cost-aware portfolio overlays with volatility targeting and risk control, letting you build overlays that account for trading costs at your execution venue.","q":"How do I use GARCH for trading with this toolkit?"},{"a":"garch-volatility-toolkit lets you compare GARCH vs GJR to understand leverage effect in volatility modeling. GJR(1,1) captures asymmetry: negative shocks drive volatility clustering more than positive ones. Standard GARCH(1,1) treats all shocks equally. For crypto, where volatility spikes on downside moves, GJR often fits better. The toolkit shows which model suits your asset and regime.","q":"What is the leverage effect and how does GJR model it differently than GARCH?"},{"a":"garch-volatility-toolkit teaches no-lookahead walk-forward backtesting methodology and leakage traps. Fit your GARCH or GJR model on historical data, forecast one step ahead, then roll forward by one period. Never fit on future data or use today's return to predict today's volatility. The toolkit demonstrates proper implementation on BTC, ETH, SOL, and AVAX to avoid overfitting and false performance claims.","q":"What walk-forward volatility prediction methodology does the toolkit teach?"},{"a":"garch-volatility-toolkit implements inverse vol scaling for position sizing: when volatility forecasts rise, reduce position size; when they fall, increase it. This keeps portfolio risk stable. However, turnover costs matter: GJR vol-scaling works at institutional rates (2bps) but erodes at retail spreads (7bps). The toolkit is cost-aware, showing you the breakeven spread for profitability at your venue.","q":"How does inverse vol scaling affect position sizing and turnover costs?"},{"a":"garch-volatility-toolkit implements DCC correlation de-weighting for multi-asset portfolio construction. When correlations rise (stress regimes), the overlay reduces weights on correlated assets to control tail risk. Testing shows DCC de-weighting adds minimal economic value in most regimes, so the toolkit helps you decide whether the complexity justifies the cost for your strategy.","q":"What role does DCC correlation de-weighting play in portfolio construction?"}],"shadow_tags":["time-series-modeling","risk-targeting","backtesting-framework","cost-aware-trading","ensemble-methods","leverage-asymmetry","correlation-dynamics","turnover-optimization","crypto-futures","walk-forward-validation"],"summary_rewrite":"Build volatility forecasts using GARCH(1,1) and GJR(1,1) models with walk-forward fitting, then apply them to scale positions inversely to risk. The toolkit includes tested recipes for univariate fits, DCC correlation overlays, and position-sizing logic on BTC, ETH, SOL, and AVAX futures data. Results are cost-dependent: GJR vol-scaling works at institutional rates (2bps) but erodes at retail spreads (7bps); DCC de-weighting adds minimal economic value."},"files":[{"bytes":12262,"path":"plugins/garch-volatility-toolkit/skills/garch-vol-recipes/SKILL.md","sha256":"3d34e24f2d2d1d6c5046969a5a6279b32a354eddd9b49bb3bea5519f10d8b639","url":"https://skillfed.io/files/terrylica/cc-skills/garch-vol-recipes/98c35f2e/SKILL.md"}],"id":"terrylica/cc-skills/garch-vol-recipes","links":{"html":"https://skillfed.io/terrylica/cc-skills/garch-vol-recipes","md":"https://skillfed.io/terrylica/cc-skills/garch-vol-recipes.md","repo":"https://github.com/terrylica/cc-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":8,"language":"Shell","last_updated":"2026-07-28","license":"MIT","name":"garch-volatility-toolkit","publisher":"terrylica","stars":58},"relations":{"similar":[{"id":"HKUDS/Vibe-Trading/quant-statistics"},{"id":"JoelLewis/finance_skills/volatility-modeling"},{"id":"agiprolabs/claude-trading-skills/volatility-modeling"},{"id":"agiprolabs/claude-trading-skills/regime-detection"},{"id":"agiprolabs/claude-trading-skills/mean-reversion"},{"id":"HKUDS/Vibe-Trading/ml-strategy"},{"id":"JoelLewis/finance_skills/historical-risk"},{"id":"longbridge/skills/longbridge-quant"},{"id":"HKUDS/Vibe-Trading/hedging-strategy"},{"id":"agiprolabs/claude-trading-skills/feature-engineering"}]},"slug":{"owner":"terrylica","repo":"cc-skills","skill":"garch-vol-recipes"},"version":"98c35f2e"}