Modelando la volatilidad del diferencial TED: Una evaluación de pronósticos de modelos con heterocedasticidad condicional[Modeling the volatility of the TED spread: An assessment of model forecasts with conditional heteroscedasticity]

This document evaluates the predictive power of two models for the TED spread, an ARMA model (Autoregressive–moving-average model) that only considers the conditional mean and an ARMA-GARCH-M model (Autoregressive model with conditional heteroscedasticity) that considers both the mean and the conditional variance, in order to determine if there is loss of information by not considering the variance in the calculation of the mean, taking as criteria the mean square error (ECM), the root mean square error (RECM), and the Diaebold and Mariano test (DM). The results obtained indicate that all the forecasts show a fairly low ECM, a lower RECM than that of the benchmark model (Random walk model) and the DM test indicates that the ARMA model presents a better fit compared to the ARMA-GARCH-M model. This leads us to conclude that despite the fact that the TED spread series presents volatility, there are no significant losses in short-term forecasts, considering only the conditional mean.