The ARCH model, or Autoregressive Conditional Heteroskedasticity model, is a statistical tool used to analyze time series data, particularly in finance. It helps to model and forecast the volatility of asset returns, allowing researchers and analysts to understand how the variability of a financial series changes over time. Developed by Robert Engle in 1982, the ARCH model assumes that the current volatility is influenced by past error terms. This means that periods of high volatility can be followed by more high volatility, while periods of low volatility can lead to more low volatility, capturing the clustering effect often seen in financial markets.