# Known Use Cases (KUC)

Total: **9**

## `KUC-101`
**Source**: `examples/bootstrap_examples.ipynb`

Computes statistical inference (confidence intervals, standard errors) for the Sharpe Ratio using bootstrap methods to quantify uncertainty in risk-adjusted performance metrics.

## `KUC-102`
**Source**: `examples/multiple-comparison_examples.ipynb`

Compares 500 predictive models against a benchmark using the Superior Predictive Ability (SPA) test to determine if any models significantly outperform the benchmark.

## `KUC-103`
**Source**: `examples/unitroot_cointegration_examples.ipynb`

Tests for cointegration relationships between WTI and Brent crude oil prices to identify mean-reverting spread opportunities using Engle-Granger and Phillips-Ouliaris tests.

## `KUC-104`
**Source**: `examples/unitroot_examples.ipynb`

Tests for stationarity in credit spreads (BAA-AAA) using Augmented Dickey-Fuller tests to determine if mean-reversion trading strategies are applicable.

## `KUC-105`
**Source**: `examples/univariate_forecasting_with_exogenous_variables.ipynb`

Forecasts univariate time series using Autoregressive models with exogenous variables (ARX) to capture the impact of external factors on the target variable.

## `KUC-106`
**Source**: `examples/univariate_using_fixed_variance.ipynb`

Demonstrates how to specify a HARX mean model with fixed/external variance inputs and iteratively fit volatility models using the estimated conditional volatility.

## `KUC-107`
**Source**: `examples/univariate_volatility_forecasting.ipynb`

Forecasts future volatility of S&P 500 returns using GARCH models, including multi-step ahead forecasts and rolling window out-of-sample predictions.

## `KUC-108`
**Source**: `examples/univariate_volatility_modeling.ipynb`

Fits and compares different GARCH volatility model specifications (symmetric, asymmetric, power) with various error distributions to characterize S&P 500 return volatility dynamics.

## `KUC-109`
**Source**: `examples/univariate_volatility_scenarios.ipynb`

Generates multiple volatility scenarios for NASDAQ returns using simulation-based forecasting methods, useful for risk management and option pricing applications.