quant-statistics

To test stationarity, an ADF unit-root test evaluates whether your price or return series has a unit root. This test produces a concise stationarity report indicating if the data is stable for quantitative modeling.

Yes, you can use the GARCH volatility modeling feature to fit and forecast volatility. By applying the arch Python library, it models changing variance over time to produce forecasts used for financial risk management.

Cointegration testing identifies long-run equilibrium relationships between two price series. By evaluating this statistical equilibrium, it generates hedge ratios and pair-trading signals, allowing you to trade based on mean-reversion opportunities.

Yes, you need pandas, statsmodels, arch, and numpy to run regression diagnostics. These Python libraries execute the analysis that detects heteroskedasticity, autocorrelation, and multicollinearity in your quantitative models.

The best way to calculate confidence intervals for a Sharpe ratio is using bootstrap methods. Bootstrap resampling provides robust confidence intervals by repeatedly sampling your trading strategy returns to assess statistical significance.

Your trading strategy needs multiple-testing corrections to avoid false positives during significance evaluation. Applying a hypothesis-testing framework adjusts p-values across multiple tests, ensuring robust statistical significance validation for your strategies.