# Understanding the GARCH Process: Key Uses in Financial Volatility

![ GARCH](https://www.investopedia.com/thmb/WRGYj_WMBidEaAzdz4Gcz7XaBD4=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/GARCH-9d737ade97834e6a92ebeae3b5543f22.png)

*— Investopedia / Julie Bang*

## What Is the GARCH Process?

The generalized autoregressive conditional heteroskedasticity (GARCH) process is an econometric model for estimating volatility in financial markets, developed in 1982 by Nobel laureate [Robert F. Engle](https://www.investopedia.com/terms/r/robert-f-engle-iii.asp). GARCH is widely used by financial institutions to forecast returns, optimize portfolios, and manage the risk of stocks, bonds, and other assets.

Unlike traditional models that rely on historical [volatility](https://www.investopedia.com/terms/v/volatility.asp), GARCH adapts to changing market conditions, offering a more practical and real-world context for predicting financial instruments' prices and rates.

### Key Takeaways:

*   The GARCH model, created by Nobel laureate Robert F. Engle, is preferred by financial professionals for its real-world applicability in predicting market volatility to guide investment decisions.
*   Unlike homoskedastic models that assume constant volatility, the GARCH process accounts for changing volatility and past variances, making it more suitable for analyzing asset returns.
*   GARCH models are valuable during financial crises, as they provide insights into volatility patterns that can affect pricing, return predictions, and risk management strategies.
*   Financial institutions leverage GARCH models to refine asset pricing, forecast investment returns, and optimize portfolios by factoring in past market behaviors and current economic conditions.  
    

## How GARCH Models Analyze Financial Volatility

[Heteroskedasticity](https://www.investopedia.com/terms/h/heteroskedasticity.asp) describes the irregular pattern of variation of an error term, or variable, in a statistical model. Essentially, where there is heteroskedasticity, observations do not conform to a [linear pattern](https://www.investopedia.com/terms/l/linearrelationship.asp). Instead, they tend to cluster.

This means that its conclusions and predictions won't be reliable. GARCH is a statistical model that can be used to analyze a number of different types of financial data, for instance, macroeconomic data. Financial institutions typically use this model to estimate the volatility of returns for stocks, bonds, and [market indices](https://www.investopedia.com/terms/m/marketindex.asp). They use the resulting information to determine pricing, judge which assets will potentially provide higher returns, and forecast the returns of current investments to help in their asset allocation, hedging, risk management, and portfolio optimization decisions.

A GARCH model typically uses three steps. The first is to estimate a best-fitting [autoregressive](https://www.investopedia.com/terms/a/autoregressive.asp) model. The second is to compute [autocorrelations](https://www.investopedia.com/terms/a/autocorrelation.asp) of the [error term](https://www.investopedia.com/terms/e/errorterm.asp). The third step is to test how significant these factors are.

Two other widely used approaches to estimating and predicting financial volatility are the classic [historical volatility](https://www.investopedia.com/terms/h/historicalvolatility.asp) (VolSD) method and the [exponentially weighted moving average](https://www.investopedia.com/terms/e/ema.asp) volatility (VolEWMA) method.

## Why GARCH Models Excel in Predicting Asset Returns

GARCH models differ from [homoskedastic](https://www.investopedia.com/terms/h/homoskedastic.asp) ones, which assume constant volatility and are used in basic [OLS analysis](https://www.investopedia.com/terms/l/least-squares.asp). OLS aims to minimize the deviations between data points and a regression line to fit those points. Asset returns show that volatility changes over time and depends on past variance, so homoskedastic models are not optimal.

GARCH processes, because they are autoregressive, depend on past squared observations and past variances to model for current variance. GARCH processes are widely used in finance due to their effectiveness in modeling asset returns and inflation. GARCH aims to minimize errors in forecasting by accounting for errors in prior forecasting and enhancing the accuracy of ongoing predictions.

## Real-World Application: GARCH Models in Financial Markets

GARCH models show how market volatility increases during crises or events and decreases during calm periods. Before a crisis like the one in 2007, stock returns may appear stable on a chart.

In the period following the onset of a crisis, however, returns may swing wildly from negative to positive territory. Moreover, the increased volatility may be predictive of volatility going forward. Volatility may then return to levels resembling that of pre-crisis levels or be more uniform going forward. A simple regression model does not account for this variation in volatility exhibited in financial markets. It is not representative of the "[black swan](https://www.investopedia.com/terms/b/blackswan.asp)" events that occur more often than predicted.

## The Bottom Line

The GARCH process is a key econometric tool for estimating volatility in financial markets, offering a more realistic view of price and rate movements than traditional homoskedastic models. Its adaptability makes it especially valuable during periods of market stress, such as financial crises.

Financial institutions use GARCH to calculate return volatility for stocks, bonds, and indices, supporting pricing, asset allocation, and risk management decisions. By estimating autoregressive models, computing autocorrelations, and testing for significance, GARCH captures changing volatility patterns that simpler models cannot.