What Is the GARCH Process?

What Is the GARCH Process?

Let me explain the generalized autoregressive conditional heteroskedasticity (GARCH) process to you—it's an econometric concept I came across, developed back in 1982 by economist Robert F. Engle, who won the Nobel Memorial Prize for Economics in 2003. Essentially, GARCH is a method you can use to estimate volatility in financial markets.

You'll find several variations of GARCH modeling out there. As someone who's looked into this, I can tell you that financial professionals often choose the GARCH process because it offers a more practical, real-world perspective compared to other models when you're trying to predict prices and rates of financial instruments.

Understanding the GARCH Process

Heteroskedasticity refers to the irregular pattern of variation in an error term or variable within a statistical model. What this means for you is that when heteroskedasticity is present, your observations don't follow a linear pattern—they cluster instead.

This clustering leads to unreliable conclusions and predictive values from the model. That's where GARCH comes in: it's a statistical model you can apply to various types of financial data, including macroeconomic data. Financial institutions rely on it to estimate volatility in returns for stocks, bonds, and market indices. From there, they use that data to set prices, identify assets with potential higher returns, and forecast outcomes for current investments, which directly informs their decisions on asset allocation, hedging, risk management, and portfolio optimization.

The general process for building a GARCH model involves three steps: first, estimate a best-fitting autoregressive model; second, compute autocorrelations of the error term; and third, test for significance.

You should also know about two other common methods for estimating and predicting financial volatility: the classic historical volatility (VolSD) method and the exponentially weighted moving average volatility (VolEWMA) method.

GARCH Models Best for Asset Returns

GARCH processes stand apart from homoskedastic models, which assume constant volatility and are typically used in basic ordinary least squares (OLS) analysis. OLS works by minimizing deviations between data points and a regression line to fit those points. But with asset returns, volatility often varies over time and depends on past variance, making homoskedastic models less than ideal.

Since GARCH is autoregressive, it relies on past squared observations and past variances to model current variance. That's why it's so widely used in finance—it's effective for modeling asset returns and inflation. The goal with GARCH is to minimize forecasting errors by incorporating errors from previous forecasts, which improves the accuracy of your ongoing predictions.

Example of the GARCH Process

GARCH models capture how volatility in financial markets can shift, becoming more intense during financial crises or major world events, and calmer during periods of steady economic growth. If you plot stock returns, for instance, they might appear fairly uniform in the years before a crisis like the one in 2007.

But once the crisis hits, returns can swing dramatically from negative to positive. This heightened volatility often predicts more volatility ahead. Eventually, it might settle back to pre-crisis levels or stabilize. A simple regression model doesn't handle this kind of volatility variation in financial markets—it's not equipped for those unexpected 'black swan' events that happen more frequently than you'd think.