time-series-analysis

Forecast financial time series by applying ARIMA for price prediction and GARCH for volatility dynamics. Input historical data, select models using information criteria, run diagnostic checks, and generate short- to medium-term forecasts across rolling windows.

Cointegration analysis identifies long-term equilibrium relationships between multiple financial time series. Use it for pairs or basket trading strategies to confirm that separate asset price series move together over time, enabling mean-reversion opportunities.

Test for stationarity by running diagnostic checks on your historical financial series. Stationarity testing determines whether statistical properties like mean and variance remain constant, which is required before fitting ARIMA or GARCH models.

Detect structural breaks in asset returns by applying diagnostic checks for shifts in volatility and statistical properties. This identifies regime changes and volatility persistence, informing risk management and model stability assessments.

Model volatility persistence using GARCH family models on historical returns. This captures changing variance over time, assesses risk dynamics, and detects regime shifts to support medium- to long-term risk management strategies.

Diagnostic checks for autocorrelation and heteroskedasticity validate model assumptions by detecting residual patterns and varying variance. These tests ensure your ARIMA and GARCH forecasts are statistically reliable and free from systematic errors.