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quant-statistics

Perform ADF unit-root tests and GARCH volatility modeling on financial time-series data.

Updated Jul 29, 2026
Search Tags:#finance#statistics#econometrics#garch#timeseries#quant
One-click install
npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill quant-statistics-santoosaraujo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/santoosaraujo/vibe-trading-claude/tree/main/.claude/skills/quant-statistics
Command: npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill quant-statistics-santoosaraujo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires statsmodels, arch, numpy, pandas.

What problem does it solve?

This skill addresses the challenge of applying rigorous statistical methods to financial time-series data, ensuring that investment strategies are built on sound mathematical foundations rather than spurious correlations.

Core Features & Use Cases

  • Time-Series Analysis: Perform ADF unit-root tests, cointegration checks, and Granger causality tests to validate data stationarity and long-run relationships.
  • Volatility Modeling: Implement GARCH(1,1) and its variants to forecast market volatility and assess risk persistence.
  • Regression Diagnostics: Automatically detect heteroskedasticity, autocorrelation, and multicollinearity to ensure model reliability.
  • Use Case: Use this skill to verify if two assets are cointegrated before deploying a pair-trading strategy, or to estimate the half-life of a mean-reverting spread.

Quick Start

Use the quant-statistics skill to perform an ADF unit-root test on the provided price series data.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema
How do I test if two assets are cointegrated for a pair trading strategy?▼

You can perform cointegration checks and ADF unit-root tests on the price series to validate stationarity and identify long-run relationships, ensuring your pair trading strategy avoids spurious correlations.

What is the best way to forecast market volatility using GARCH models in Python?▼

Forecasting market volatility with GARCH models requires using the arch library in Python to implement GARCH(1,1) and its variants, which estimate risk persistence and project future market volatility for financial time-series data.

How do I detect heteroskedasticity and autocorrelation in financial time-series regression?▼

To detect heteroskedasticity and autocorrelation in financial time-series regression, apply robust regression diagnostics that automatically test model reliability and validate mathematical foundations using statsmodels.

Do I need statsmodels and arch installed to run quantitative finance statistics?▼

Yes, you need statsmodels, arch, numpy, and pandas installed to execute complex statistical inference, GARCH volatility modeling, and unit-root testing for quantitative finance analysis.

When should I use Granger causality tests for time-series analysis?▼

You should use Granger causality tests during time-series analysis when you need to validate data stationarity and determine if one time series can forecast another, ensuring strategies rely on true relationships rather than spurious correlations.

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