statistics-timeseries

To forecast volatility in time-series data, apply GARCH models to historical price series using Python libraries like arch. This method models volatility dynamics and generates forecasts to support risk assessment and trading strategy development.

To analyze return distributions for trading, apply descriptive statistics to historical price data to extract statistical features. This distribution analysis reveals risk metrics and baseline characteristics across time horizons and asset classes for strategy development.

Yes, you can use statsmodels and arch Python packages for backtesting trading strategies. The workflow applies ARIMA and GARCH modeling to historical price data, generating reproducible inputs and outputs that establish backtesting baselines for trading strategies.

The best way to combine factor models with volatility forecasting for risk assessment is applying regression approaches alongside GARCH models to historical price data. This unified statistical analysis evaluates relationships and volatility dynamics to produce comprehensive risk metrics.