What does GARCH models specifically forecast?

GARCH models, which stand for Generalized Autoregressive Conditional Heteroskedasticity models, are specifically designed to forecast the variance of a financial time series based on past values and past forecasted variances. The essence of a GARCH model lies in its ability to model and predict how volatility changes over time, particularly in response to unexpected returns.

The choice indicating variances based on recent unexpected values highlights the model's focus on how new information impacts the level of volatility. In GARCH models, past squared returns (which represent unexpected values) are utilized to update estimates of future variances. This characteristic makes GARCH particularly useful in financial contexts where volatility is not constant but changes through time in reaction to new information.

When considering the other options, the forecasting of future expected returns is not the primary role of GARCH models; they do not predict stock returns but rather the variance of those returns. Additionally, the average price of a financial asset is not something GARCH models address, as they specifically deal with variability rather than price levels. Lastly, while market factors can influence volatility, GARCH models incorporate both market factors and the history of shocks to variances, making them more comprehensive than simply focusing on market influences.

Thus, the choice