The article discusses different ways to estimate volatility in financial data using GARCH models. It focuses on how to estimate these models accurately, especially when the data is not stable over time. The researchers looked at two main methods, one called quasi-maximum likelihood and the other called least absolute deviation. They found that these methods can be used to estimate GARCH models effectively, even when the data is not stationary.