Volatility is an important issue for companies, policy-makers, and researches. Autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) models are frequently used to study volatility. However, forecasting efficiency tends to fail when complex data is used. This paper proposes the use of ordered weighted average (OWA) operators in combination with ordinary least squares (OLS) to create an estimator that can treat high degrees of uncertainty. In the application of the ARCH-GARCH models, we develop approaches with the OWA and the induced OWA operator. Some further generalizations are also developed by using generalized means. The main advantage of this new methodology is to add additional information to the process of estimating the models according to the attitudinal character of the decision-maker. Finally, the work presents an application in the volatility of the MX/US exchange rate, where the efficiency of the OWA operators in forecasting is proved.

对于公司，决策者和研究而言，波动性是重要的问题。自回归条件异方差（ARCH）和广义ARCH（GARCH）模型通常用于研究波动率。但是，使用复杂数据时，预测效率往往会失败。本文提出结合有序加权平均（OWA）运算符和普通最小二乘（OLS）来创建可以处理高度不确定性的估计器。在ARCH-GARCH模型的应用中，我们使用OWA和归纳的OWA运算符开发了方法。还可以通过使用广义手段来开发一些进一步的概括。这种新方法的主要优点是可以根据决策者的态度特征在估计模型的过程中添加其他信息。最后，