Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Process

What is the 'Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Process '

The generalized autoregressive conditional heteroskedasticity (GARCH) Process is an econometric term developed in 1982 by Robert F. Engle, an economist and 2003 winner of the Nobel Memorial Prize for Economics to describe an approach to estimate volatility in financial markets. There are several forms of GARCH modeling. The GARCH process is often preferred by financial modeling professionals because it provides a more real-world context than other forms when trying to predict the prices and rates of financial instruments.

BREAKING DOWN 'Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Process '

The general process for a GARCH model involves three steps. The first is to estimate a best-fitting autoregressive model; secondly, compute autocorrelations of the error term and lastly, test for significance.

GARCH models are used by financial professionals in several arenas including trading, investing, hedging and dealing. Two other widely-used approaches to estimating and predicting financial volatility are the classic historical volatility (VolSD) method and the exponentially weighted moving average volatility (VolEWMA) method.

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