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 by allowing the variance of the error term to change over time, depending on past error terms. This is particularly useful for understanding periods of high and low volatility in financial markets. Developed by Robert Engle in 1982, the ARCH model assumes that the current volatility is influenced by previous periods' squared returns. This approach provides a more accurate representation of financial data, as it captures the clustering of volatility often observed in real-world markets, making it a valuable tool for risk management and investment strategies.