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GARCH Process: How It’s Used in Different Forms?

File Photo: GARCH Process: How It's Used in Different Forms?
File Photo: GARCH Process: How It's Used in Different Forms? File Photo: GARCH Process: How It's Used in Different Forms?

What is the GARCH process?

Robert F. Engle, a 2003 Nobel Memorial Prize-winning economist, coined “garch” in 1982. The GARCH method estimates financial market volatility. There are various GARCH models. Financial experts use the GARCH method to anticipate financial instrument prices and rates because it gives a better real-world context.

Knowing the GARCH Process

Heteroskedasticity is the irregular fluctuation of an error term or variable in a statistical model. When there is heteroskedasticity, observations do not follow a linear pattern. Instead, they cluster.

Conclusions and predictions from the model will be unreliable. The GARCH statistical model can examine macroeconomic data and other financial data. Financial institutions commonly utilize this model to predict return volatility for stocks, bonds, and market indexes. The collected data helps them price assets, predict their returns, and allocate, hedge, risk manage, and optimize their portfolios.

The general GARCH model procedure has three steps. Step one is to estimate the best-fitting autoregressive model. The second step is to calculate error-term autocorrelations; the third is to test significance.

Two popular methods for assessing and predicting financial volatility are the historical volatility (VolSD) and exponential weighted moving average (VolEWMA) methods.

Best Asset Return Model: GARCH

GARCH processes are not the same as homoskedastic models, which use ordinary least squares (OLS) to analyze data and assume that volatility stays the same over time. OLS minimizes data point deviations and fits a regression line. A homoskedastic model is unsatisfactory for asset returns since volatility varies and depends on historical variance.

The GARCH processes model presents variance using previous squared observations and variances since they are autoregressive. GARCH processes model asset returns and inflation well, making them popular in finance. GARCH accounts for earlier forecasting errors and improves continuing forecasts to reduce forecasting errors.

Example of GARCH Process

In GARCH models, financial markets are more volatile during financial crises or international events and less volatile during relative calm and stable economic development. Stock returns may appear consistent before a financial crisis like 2007 on a plot of returns.

After a crisis, returns may swing widely from negative to positive. Increased volatility may also foreshadow future volatility. Volatility may then revert to pre-crisis levels or become more consistent. A simple regression model cannot explain financial market volatility. It does not reflect “black swan” occurrences that occur more often than expected.


  • The generalized autoregressive conditional heteroskedasticity (GARCH) process estimates financial market volatility.
  • Using the model, financial institutions estimate stock, bond, and other investment vehicle return volatility.
  • When predicting financial instrument prices and rates, the GARCH process is more realistic than other models.

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