garch-volatility-toolkit
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.
garch-volatility-toolkit forecasts asset volatility using GARCH and GJR models to dynamically scale portfolio positions.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-28
garch-volatility-toolkit forecasts asset volatility using GARCH and GJR models to dynamically scale portfolio positions. 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.
Use it when
- garch-volatility-toolkit enables you to forecast asset volatility using GARCH(1,1) and GJR(1,1) models for dynamic position sizing.
- garch-volatility-toolkit lets you compare GARCH vs GJR to understand leverage effect in volatility modeling.
Verify before relying
Read SKILL.md below before installing (4 files). Open directory: indexed for reading, not audited.
Install
terrylica/cc-skills/garch-vol-recipes · repository language: Shell
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What is garch-volatility-toolkit and what does it do?
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.
How do I use GARCH for trading with this toolkit?
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.
What is the leverage effect and how does GJR model it differently than GARCH?
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.
What walk-forward volatility prediction methodology does the toolkit teach?
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.
How does inverse vol scaling affect position sizing and turnover costs?
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.
What role does DCC correlation de-weighting play in portfolio construction?
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.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
GARCH Volatility Toolkit
> Self-Evolving skill — if a recipe drifts from what actually reproduces (model spec, cost regime, campaign Sharpe numbers, library API), fix this SKILL.md in the same change; see the Post-Execution Reflection at the bottom.
Walk-forward GARCH(1,1) and GJR(1,1) recipes for volatility forecasting and portfolio construction. Tested on 20 seeds, 2025-26 test window, 2bps and 7bps cost regimes.
> Campaign Results (honest): A negative-to-marginal result set, not a deployable edge. GJR > inverse vol-sizing helps at LOW cost only (ensemble Sharpe +0.49→+1.00 @2bps) but is COST-FRAGILE — > its +35% turnover erases the benefit at retail 7bps (Δ+0.02, p=0.54, coin-flip). DCC de-weighting is > economically immaterial (+0.05–0.07). GARCH-as-features is a flat
(truncated - see the full file via the links below)
File tree — 4 files
plugins/garch-volatility-toolkit/skills/garch-vol-recipes/SKILL.md
plugins/garch-volatility-toolkit/skills/garch-vol-recipes/references/CAMPAIGN_VERDICT.md
plugins/garch-volatility-toolkit/skills/garch-vol-recipes/references/garch_angleB_verified.json
plugins/garch-volatility-toolkit/skills/garch-vol-recipes/references/garch_angleC_verified.json
Let your AI agent find skills like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 56,283 agent skills by what they can do, searchable in plain language.
wish › “Forecast asset volatility using GARCH/GJR models for dynamic position sizing”
Give your agent the search over MCP, or paste the wish link into any chat. No install? Search from any chat →
Related skills
Quant-statistics provides time-series testing and volatility modeling tools for quantitative investing. It covers stationarity detection via ADF tests, cointegration analysis for pair trading, GARCH volatility forecasting, and regression diagnostics including heteroskedasticity and autocorrelation checks.
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.
Correlation-regime answers temporal questions about market fusion that snapshots cannot: when assets become highly correlated, what that means for position sizing, which asset moved first in a crisis, and who quietly rewired market relationships. The skill uses edge-density state machines with hysteresis to emit clean regime cycles with few false alarms, validated on eight historical crises across crypto and equities.
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 50–150% annualized volatility levels.
Regime Detection classifies market conditions across volatility and trend axes to help you choose the right strategy for current conditions. It combines simple approaches like ATR percentiles and ADX with statistical methods including Hurst exponent and change-point detection, with tuning for crypto's faster regime shifts.
Correlation Analysis helps traders and portfolio managers understand how crypto assets move in tandem across different market conditions. It covers Pearson, Spearman, and Kendall correlation methods, rolling correlation windows for regime detection, hierarchical clustering to group similar assets, and tail dependence analysis to reveal crash co-movement that standard metrics miss. Use it to build diversified portfolios, generate pairs trading signals, and protect against concentration risk.
More skills mean-reversion (MIT) · ml-strategy (MIT) · historical-risk (MIT) · longbridge-quant (MIT) · hedging-strategy (MIT) · feature-engineering (MIT)