quant-statistics

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.

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.

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.

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.

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.

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.