Unlike standard regression models that assume constant variance over time (homoscedasticity), ARCH models recognize that financial data often experiences "volatility clustering"—low volatility periods followed by low volatility, and high volatility periods followed by high volatility. The Core Concept: Modeling Variance
stands for Autoregressive Conditional Heteroscedasticity. It is a statistical framework designed specifically to model time series data characterized by non-constant variance (heteroscedasticity) that depends on past observations. arch models
In the world of finance, unpredictability is the only constant. While traditional statistical models excel at forecasting the mean return of an asset, they often fail to capture the periods of high turbulence—the "volatility clusters"—that define real-world markets. Introduced by Nobel laureate Robert Engle in 1982 , Autoregressive Conditional Heteroscedasticity () models revolutionized how analysts, risk managers, and economists handle this complexity. In the world of finance, unpredictability is the
The Black-Scholes model assumes constant volatility—which traders know is false. GARCH-based option pricing models (e.g., Heston-Nandi) better capture the volatility smile. Heston-Nandi) better capture the volatility smile.