{"enrichment":{"faq":[{"a":"Quant-statistics provides ADF (Augmented Dickey-Fuller) unit root testing to detect stationarity in time series data. This is essential for identifying mean-reverting assets and avoiding spurious regression in quantitative strategies. The skill enables researchers to validate whether price series or spreads are stationary before building statistical arbitrage models.","q":"What ADF unit root test and stationarity capabilities does quant-statistics offer?"},{"a":"Quant-statistics includes cointegration analysis tools designed for pair trading strategy development. These tests identify long-term equilibrium relationships between two assets, calculate hedge ratios, and detect mean-reverting spreads. Cointegration testing helps traders establish when two securities move together and when deviations signal trading opportunities.","q":"How does quant-statistics support cointegration testing for pair trading?"},{"a":"Yes, quant-statistics supports GARCH and variant models for volatility forecasting and risk management. The toolkit enables modeling of time-varying volatility, capturing volatility clustering in financial returns, and generating forward-looking volatility estimates. EGARCH variants are also available for capturing asymmetric volatility effects in downturns.","q":"Can quant-statistics model and forecast volatility using GARCH?"},{"a":"Quant-statistics validates regression models through comprehensive diagnostic tests including heteroskedasticity detection, autocorrelation checks via Ljung-Box testing, and residual analysis. These diagnostics ensure regression assumptions hold and help identify model specification issues before deployment in quantitative strategies.","q":"What regression diagnostics does quant-statistics provide for model validation?"},{"a":"Quant-statistics employs bootstrap methods to estimate confidence intervals and assess statistical significance without relying on parametric assumptions. This approach is particularly valuable for testing Sharpe ratio significance, factor returns, and other non-normally distributed financial metrics in quantitative research.","q":"How does quant-statistics estimate confidence intervals and test significance?"},{"a":"Quant-statistics includes hypothesis testing and multiple-testing correction methods such as FDR (False Discovery Rate) for factor research. These corrections prevent false discoveries when testing many factors simultaneously, ensuring robust factor analysis and reducing the risk of overfitting in quantitative finance applications.","q":"Does quant-statistics support multiple-testing corrections for factor research?"}],"shadow_tags":["time-series-analysis","stationarity-testing","volatility-forecasting","pair-trading-signals","regression-validation","statistical-inference","backtesting-rigor","factor-research","risk-modeling"],"summary_rewrite":"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."},"files":[{"bytes":14777,"path":"agent/src/skills/quant-statistics/SKILL.md","sha256":"45f3c6d5cd6f147e9668f39a2903c3023fe1332931627712410ec43922746bab","url":"https://skillfed.io/files/HKUDS/Vibe-Trading/quant-statistics/e77e3de1/SKILL.md"}],"id":"HKUDS/Vibe-Trading/quant-statistics","links":{"html":"https://skillfed.io/HKUDS/Vibe-Trading/quant-statistics","md":"https://skillfed.io/HKUDS/Vibe-Trading/quant-statistics.md","repo":"https://github.com/HKUDS/Vibe-Trading"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":4557,"language":"Python","last_updated":"2026-07-27","license":"MIT","name":"quant-statistics","publisher":"HKUDS","stars":28096},"relations":{"similar":[{"id":"jaechang-hits/SciAgent-Skills/statsmodels-statistical-modeling"},{"id":"terrylica/cc-skills/garch-vol-recipes"},{"id":"agiprolabs/claude-trading-skills/mean-reversion"},{"id":"HKUDS/Vibe-Trading/correlation-analysis"},{"id":"wangyendt/wayne-skills/statistics"},{"id":"agiprolabs/claude-trading-skills/cointegration-analysis"},{"id":"HKUDS/Vibe-Trading/risk-analysis"},{"id":"aj-geddes/useful-ai-prompts/time-series-analysis"},{"id":"foryourhealth111-pixel/Vibe-Skills/statsmodels"},{"id":"drshailesh88/integrated_content_OS/statsmodels"}]},"slug":{"owner":"HKUDS","repo":"Vibe-Trading","skill":"quant-statistics"},"version":"e77e3de1"}