The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process is a statistical method commonly used in the finance industry to model and predict the volatility in a financial market. This model is advantageous to use when predicting the asset prices of stocks and bonds as well as other investment vehicles since it considers the changing market conditions and predicts returns based on the most accurate data.

The GARCH process works by using a combination of past and current data points to predict future returns. The process is based on two parameters—the autoregressive and the conditional variance components. The approach allows the investor to model the impact of past movements on future returns by tracking the volatility of financial instruments over time. The model allows for a more precise analysis of asset pricing and risk management. Furthermore, GARCH models are capable of forecasting future returns of an instrument based on past movements.

The structure of the GARCH process combines the autoregressive component and the conditional variance component. The autoregressive component helps the model capture the effects of the shocks to the market, while the conditional variance component captures the volatilities from the shocks. The model looks at past returns and variances and uses them to project future returns and volatilities. It does this by assuming that the variance of future returns is a function of past returns and volatilities as well as current market conditions.

Given its structure, the GARCH process is particularly well-suited to measure and predict the risk of an investment vehicle. The model provides a robust approach to estimating returns and the accompanying risks of different investment vehicles. By calculating these metrics, investors can make informed decisions when investing.

Overall, the GARCH process is a powerful tool used by financial institutions to assess and forecast the volatility of financial markets. It has been widely used by investors and institutions to ascertain risks associated with investing in different financial instruments. The model offers an effective and precise approach to forecasting prices and returns of stocks and bonds, which comes with a certain degree of accuracy.