quant-statistics

Perform quantitative statistics on financial time-series with unit-root and cointegration tests.

Updated May 15, 2026

Search Tags:#statistics #time-series #bootstrap #hypothesis-testing #garch #cointegration #regression-diagnostics

Ready to run

npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill quant-statistics-philipcoller-777

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/philipcoller-777/Vibe-Trading-TV2/tree/main/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill quant-statistics-philipcoller-777

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provide a comprehensive suite of quantitative statistical methods for finance, including time-series testing, volatility modeling, regression diagnostics, bootstrap inference, and hypothesis testing to support research and decision-making.

Core Features & Use Cases

Time-series tests: ADF unit-root tests and cointegration assessment for stationary relationships and long-run equilibria.
Volatility modeling & diagnostics: GARCH-family models, heteroskedasticity checks, autocorrelation diagnostics.
Bootstrap & inference: Nonparametric bootstrap and hypothesis testing to assess significance and robustness in financial research.

Quick Start

Run an ADF test on a provided price series and report stationarity.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema

How do I run an ADF unit-root test on financial time-series data?▼

To run an ADF unit-root test on financial time-series, you apply the Skill's time-series testing methods to your price series to evaluate stationarity and detect long-run equilibria. The Skill reports whether the series is stationary.

What is the best way to model volatility in asset returns using GARCH variants?▼

GARCH variants model volatility in asset returns by fitting conditional variance models to financial time-series. The Skill provides GARCH-family models and autocorrelation diagnostics to assess heteroskedasticity in return data.

Can I use bootstrap inference to assess the robustness of financial hypothesis testing?▼

Bootstrap inference assesses the robustness of financial hypothesis testing by applying nonparametric bootstrap methods to your asset returns. The Skill performs resampling to evaluate significance without relying on strict distributional assumptions.

How do I check cointegration between asset prices for stationary relationships?▼

Checking cointegration between asset prices involves applying cointegration assessment methods to your time-series data. The Skill evaluates long-run equilibria and stationary relationships across multiple asset price series.

Does this approach support regression diagnostics for financial time-series modeling?▼

Regression diagnostics for financial time-series modeling are supported through the Skill's built-in diagnostic procedures. It provides heteroskedasticity checks and autocorrelation diagnostics to validate your regression models.

When should I use nonparametric bootstrap methods over standard hypothesis testing in finance?▼

Nonparametric bootstrap methods are used over standard hypothesis testing in finance when distributional assumptions are questionable. The Skill applies bootstrap inference to assess significance and robustness in financial research.