quant-statistics

What problem does it solve?

Enables quantitative researchers to apply rigorous statistical tests across time series, volatility modeling, regression diagnostics, bootstrap analysis, and hypothesis testing so that strategy insights and risk factors are grounded in defensible metrics rather than intuition alone.

Core Features & Use Cases

• Stationarity and Cointegration: Runs ADF, cointegration, and Granger causality tests to ensure series are suitable for regression and pair-trading strategies, then interprets z-score signals for spread trades.
• Volatility Modeling: Fits GARCH(1,1) plus variant summaries to forecast conditional volatility, persistence, and leverage effects in equities or crypto returns.
• Diagnostic Suite: Evaluates heteroskedasticity, autocorrelation, and multicollinearity in regression residuals, deploys Newey-West or WLS remedies, and offers bootstrap confidence intervals for Sharpe and factor premiums.
• Use Case: When backtesting a factor model, run this Skill to confirm stationarity, volatility persistence, and statistical significance before deploying capital.

Quick Start

Request the quant-statistics skill to run its ADF, cointegration, GARCH, and regression diagnostics on your latest return series.