Predictive Accuracy of Futures Options Implied Volatility: The Case of the Exchange Rate Futures Mexican Peso-Us Dollar

Abstract

RESUMENExiste una cantidad substancial de investigación destinada a pronosticar la volatilidad de los rendimientos de precios de futuros de activos financieros. Una parte significativa de la literatura muestra que pronosticar la mencionada volatilidad con certeza no es una tarea fácil, independientemente del modelo de pronóstico utilizado. En el presente trabajo de investigación se analiza el poder predictivo de varios modelos de pronósticos de volatilidad diaria para los rendimientos de los futuros del tipo de cambio Peso Mexicano-Dólar Estadounidense. Los modelos que se utilizan son: univariado GARCH; multi-variado GARCH (modelo BEKK); dos modelos de volatilidad implícita de opciones; y, un modelo de pronóstico compuesto. Diferente a otros trabajos en la literatura, ABSTRACTThere has been substantial research effort aimed to forecast futures price return volatilities of financial assets. A significant part of the literature shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of several volatility forecast models for the case of the Mexican peso-USD exchange rate futures returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), two option implied volatility models and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. Different to other works in the literature, in this paper there is a more rigorous analysis of the option implied volatilities calculations. The results show that the option implied models are superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-squared-errors. However, the results should be taken with caution given that the coefficient of determination in the regressions was relatively low. According to these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied.