Volatility Forecasts for the Mexican Peso - U.S. Dollar Exchange Rate: An Empirical Analysis of Garch, Option Implied and Composite Forecast Models
AbstractThe volatility accuracy of several volatility forecast models is examined for the case of daily spot returns for the Mexican peso - US Dollar exchange rate. The models applied are univariate GARCH, a multi-variate GARCH (the BEKK model), option implied volatilities, and a composite forecast model. The composite specification includes time-series (ARCH-type) and option implied volatility forecasts. Different to most of the literature, this paper includes a statistical evaluation of the forecast accuracy of a composite model and models that are not combined. The results show that the composite volatility forecasts are superior to the other models in terms of mean squared errors (MSE). In forecast evaluations of the MSE it was found that estimates were statistically significantly different between composite forecast estimates and its counterparts. According to these results conclusions are as follows: the composite model is superior and both type of data -historical and implied in option prices- must be used when available.
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Bibliographic InfoPaper provided by Banco de México in its series Working Papers with number 2006-04.
Date of creation: Apr 2006
Date of revision:
Composite forecast models; Exchange rates; Multivariate GARCH; Option implied volatility; Volatility forecasting;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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