Volatility Clustering refers to a phenomenon observed in financial markets where periods of high market volatility are likely to be followed by periods of high volatility, while periods of low volatility are likely to be followed by periods of low volatility. This pattern, or clustering of volatility, is often seen in stock market returns and is one of the key features in many financial time series models.

The concept is particularly important in financial modelling and risk management, as understanding volatility clustering can help in predicting forthcoming market volatility and in making informed investment decisions. These observations form the basis of models like the Auto Regressive Conditional Heteroskedasticity (ARCH) and the Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models.

The main reason behind this occurrence is that markets react to information or certain events, which leads to a rise in volatility due to increased trading activity. Once the information is fully absorbed, the market tends to calm down until the next piece of significant news arrives.