What is the '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 'GARCH Process '

Heteroskedasticity 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. Instead, they tend to cluster. The result is that the conclusions and predictive value one can draw from the model will not 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. They use the resulting information to help determine pricing and judge which assets will potentially provide higher returns, as well as to forecast the returns of current investments to help in their asset allocation, hedging, risk management and portfolio optimization decisions.

The general process for a GARCH model involves three steps. The first is to estimate a best-fitting autoregressive model. The second is to compute autocorrelations of the error term. The third step is to test for significance. 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.

Example of GARCH Process

GARCH models help to describe financial markets in which volatility can change, becoming more volatile during periods of financial crises or world events and less volatile during periods of relative calm and steady economic growth. On a plot of returns, for example, stock returns may look relatively uniform for the years leading up to a financial crisis such as the one in 2007. In the time 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 and is not representative of the "black swan" events that occur more than one would predict.

GARCH Models Best for Asset Returns

GARCH processes differ from homoskedastic models, which assume constant volatility and are used in basic ordinary least squares (OLS) analysis. OLS aims to minimize the deviations between data points and a regression line to fit those points. With asset returns, volatility seems to vary during certain periods of time and depend on past variance, making a homoskedastic model not optimal.

GARCH processes, being 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, thereby, enhancing the accuracy of ongoing predictions.

RELATED TERMS
  1. Autoregressive Conditional Heteroskedasticity ...

    Autoregressive conditional heteroskedasticity (ARCH) is a statistical ...
  2. Homoskedastic

    Homoskedastic refers to a condition in which the variance of ...
  3. Heteroskedasticity

    Heteroskedasticity, in statistics, is when the standard deviations ...
  4. Implied Volatility - IV

    The estimated volatility of a security's price derived from an ...
  5. Historical Volatility - HV

    Historical volatility is a statistical measure of the dispersion ...
  6. Volatility Ratio

    The volatility ratio is a technical measure used to identify ...
Related Articles
  1. Investing

    Volatile Stocks: Great, If You Have The Stomach

    Volatile stocks can be a lucrative opportunity for short-term traders. For buy-and-hold investors, it's a much different story.
  2. Trading

    Why Volatility is Important For Investors

    Many investors realize the stock market is a volatile place to invest their money, learn how volatility affects investors and how to take advantage of it.
  3. Investing

    3 Reasons to Ignore Market Volatility (VIX)

    If you can keep your head while those about you are losing theirs, you can make a nice return in roiling markets.
  4. Investing

    How to Take Advantage of Volatility as an Investor

    Everyone talks about the downside of volatility, but it has its benefits too, including opportunities to investment entry points at lower prices.
  5. Investing

    Roller coaster 2016 for Stocks? Exploring Global Stock Volatility

    Find out how much volatility global equity investors are in for during 2016 by seeing how much they've experienced over the past five years.
  6. Investing

    Calculating volatility: A simplified approach

    Though most investors use standard deviation to determine volatility, there's an easier and more accurate way of doing it: the historical method.
  7. Insights

    Low Volatility? You Have Options

    With volatility at record lows, options have never been cheaper.
  8. Trading

    Exploring the Exponentially Weighted Moving Average

    Learn how to calculate a metric that improves on simple variance.
  9. Investing

    5 Rules of the Road for Volatile Markets

    Following these rules of the road can help your portfolio withstand the impact of market volatility.
  10. Investing

    Understand the Risks of Trading Inverse ETFs

    Inverse ETFs sound like a great way to take advantage of market volatility. But it's important to understand how they work before you invest.
RELATED FAQS
  1. What is the difference between financial forecasting and financial modeling?

    Understand the difference between financial forecasting and financial modeling, and learn why a company should conduct both ... Read Answer >>
  2. What is the best measure of a stock's volatility?

    Understand what metrics are most commonly used to assess a security's volatility compared to its own price history and that ... Read Answer >>
  3. Volatility From the Investor's Point of View

    The increased volatility in the stock market provides greater opportunities for profit for both long and short-term traders. Read Answer >>
  4. What is the relationship between implied volatility and the volatility skew?

    Learn what the relationship is between implied volatility and the volatility skew, and see how implied volatility impacts ... Read Answer >>
  5. Which market indicators reflect volatility in the stock market?

    Stock traders use the volatility index (VIX), the average true range (ATR) indicator and Bollinger Bands to indicate volatility ... Read Answer >>
Trading Center