quant-statistics

Name: quant-statistics
Availability: InStock
Author: wudye

Apply ADF and Engle–Granger tests with GARCH volatility modeling to financial time series.

Updated May 5, 2026

Search Tags:#bootstrap #adf #garch #cointegration #stationarity #multiple testing #regression diagnostics

Ready to run

npx skills add https://github.com/wudye/traderAssistHK --skill quant-statistics-wudye

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/quant-statistics
Command: npx skills add https://github.com/wudye/traderAssistHK --skill quant-statistics-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you avoid misleading backtest and model conclusions by applying rigorous quantitative statistical tests and modeling techniques to time series, volatility, and regression residuals.

Core Features & Use Cases

Time-series testing: Run ADF unit-root tests and Engle–Granger cointegration tests to determine stationarity and long-run relationships for trading signals like pair trading.
Volatility modeling: Fit GARCH(1,1) and related variants (EGARCH, GJR-GARCH, FIGARCH) to capture volatility clustering and forecast near-term volatility.
Regression diagnostics & inference: Evaluate heteroskedasticity, autocorrelation, and multicollinearity, then use bootstrap and multiple-testing corrections to assess robustness.

Quick Start

Ask for an ADF stationarity decision, cointegration check for a pair, and a 5-day GARCH volatility forecast from your price/return series so you can validate your trading assumptions before acting.

Frequently Asked Questions about quant-statistics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I test cointegration for a pair trading spread?▼

You validate pair trading spreads by running an Engle–Granger cointegration test on two financial time series to determine their long-run relationship. This produces interpretable test decisions indicating whether the spread is stationary and statistically defensible.

How do I forecast near-term volatility using GARCH models?▼

You forecast near-term volatility by fitting GARCH(1,1) or related variants like EGARCH, GJR-GARCH, and FIGARCH to your return series. This captures volatility clustering and projects near-term confidence intervals for risk assessment.

What is the best way to check stationarity in financial time series?▼

The best way to check stationarity in financial time series is running an ADF unit-root test. This provides a clear statistical decision on whether your series has a unit root, ensuring your trading model assumptions are valid.

How do I fix misleading backtest conclusions caused by regression residuals?▼

You fix misleading backtest conclusions by running regression diagnostics including White, Breusch–Pagan, Ljung–Box, and Durbin–Watson tests. This evaluates heteroskedasticity and autocorrelation to assess model robustness.

When do I need bootstrap and multiple-testing corrections for trading research?▼

You need bootstrap and multiple-testing corrections when evaluating hypothesis testing robustness across financial time series. These methods prevent false positives and ensure your trading signal discoveries remain statistically defensible.

Does this approach support asymmetric volatility modeling for financial data?▼

Yes, this approach supports asymmetric volatility modeling through EGARCH and GJR-GARCH variants. These models capture leverage effects in financial return series where negative shocks impact volatility differently than positive shocks.