ARCH (Autoregressive Conditional Heteroscedasticity) is a statistical model
commonly used to analyze and forecast the volatility of financial time series data. It was
introduced by Robert F. Engle in the early 1980s and has since become a widely used tool in
econometrics and quantitative finance.
The ARCH model is designed to capture the timevarying volatility or heteroscedasticity observed in many financial series, where the volatility of
returns tends to cluster in periods of high volatility and dissipate during periods of low volatility.
It assumes that the conditional variance of the series is a function of past squared residuals or
error terms.