quant-statistics

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.

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.

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.

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.

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.

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.