Dr. León-Castro, Ernesto

Agricultural products have experienced sudden changes in prices in recent years as a result of volumes of production and demand at the international level. Volatility is a key element in understanding the difficulties that the market may have. However, the traditional formula for volatility only considers historical information and does not consider decision makers’ knowledge and skills. To improve this approach and obtain more accurate results consistent with the reality of the market, the ordered weighted averaging (OWA) operator is used. These new approaches are the OWA-Volatility, Induced OWA-Volatility, Heavy OWA-Volatility, Probabilistic OWA-Volatility, Induced Probabilistic OWA-Volatility and Induced Heavy OWA-Volatility. In addition, some particular cases are presented in which the aggregation process is only applied to one part of the formula or quasi-arithmetic means are used. An example of volatility calculations for corn prices in 2017 is presented.

Estimating and forecasting volatility is an important issue for financial decision-makers. Therefore, it is important to build models that adapt to the current characteristics of the time series. The ordered weighted average (OWA) has some extensions that provide interesting ways to adapt to these characteristics. This work proposes a new application that uses the simple linear regression (LR) and OWA operators in the same formulation. We use the heavy ordered weighted average (HOWA), the prioritized ordered weighted average (PrOWA), the probabilistic ordered weighted average (POWA) and their combinations with induced cooperators. The main idea in linear regression with OWA operator is to obtain an estimate and forecast that can be adaptable to situations of uncertainty and information known to the decision maker. The work analyzes the applicability of this new approach in a problem regarding exchange rate volatility forecasting, where the operators that we can use in high or low seasons are located and thus generate ranges.