Forecasting Exchange Rate Volatility

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  • Topic: Forecasting, Autoregressive conditional heteroskedasticity, Implied volatility
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Journal of Empirical Finance 19 (2012) 627–639

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Journal of Empirical Finance
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Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts☆ Guillermo Benavides a,⁎, Carlos Capistrán b

Banco de México, Mexico
Bank of America Merrill Lynch, Mexico



Article history:
Received 26 February 2010
Accepted 5 July 2012
Available online 16 July 2012
Composite forecasts
Forecast evaluation
Implied volatility
Mexican peso–U.S. dollar exchange rate
Regime switching

This paper provides empirical evidence that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, unconditional combinations, and hybrid forecasts. Superior forecasting performance is achieved by both, taking into account the conditional expected performance of each model given current information, and combining individual forecasts. The method used in this paper to produce conditional combinations extends the application of conditional predictive ability tests to select forecast combinations. The application is for volatility forecasts of the Mexican peso–US dollar exchange rate, where realized volatility calculated using intraday data is used as a proxy for the (latent) daily volatility. © 2012 Elsevier B.V. All rights reserved.

JEL classification:

1. Introduction
Even though several models are widely used by academics and practitioners to forecast volatility, nowadays there is no consensus about which method is superior in terms of forecasting accuracy (Andersen et al., 2006; Poon and Granger, 2003; Taylor, 2005). The vast majority of models can be classified in two classes: models based on time series, and models based on options. There are basically two classes of models used in volatility forecasting: models based on time series, and models based on options (Poon and Granger, 2003). Among the time series models, there are models based on past volatility, such as historical averages of

☆ We thank Alejandro Díaz de León, Antonio E. Noriega, Carla Ysusi, Carlos Muñoz Hink, the Editor and seminar participants at the 2008 Latin American Meeting of the Econometric Society at Rio de Janeiro, the XII Meeting of CEMLA's Central Bank Researchers' Network at Banco de España, the 2008 Meeting of the Society of Nonlinear Dynamics and Econometrics at the Federal Reserve Bank of San Francisco, Banco de México, ITAM, ITESM Campus Cd. de México, and Universidad del Valle de México for helpful comments. We also thank Antonio Sibaja and Pablo Bravo for helping us with the exchange rate intraday data. Andrea San Martín, Gabriel López-Moctezuma, Luis Adrián Muñiz, and Sergio Vargas provided excellent research assistance. The final draft of this paper was written while Carlos Capistrán was working at Banco de México (Central Bank of Mexico). The opinions expressed in this article are solely those of the authors and do not necessarily reflect the views of Banco de México or Bank of America Merrill Lynch. ⁎ Corresponding author at: Av 5 de Mayo # 2, Centro, México, D.F., CP. 06059, México. Tel.: +52 55 5237 2000x3877; fax: +52 55 5237 2559. E-mail address: (G. Benavides).

0927-5398/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jempfin.2012.07.001


G. Benavides, C. Capistrán / Journal of Empirical Finance 19 (2012) 627–639

squared price returns, Autoregressive Conditional Heteroskedasticity-type models (ARCH-Type), such as ARCH, GARCH, and EGARCH, and stochastic volatility models.1 Among the options based volatility models, typically called option implied volatilities (IV), there are the Black–Scholes-type models (Black and Scholes, 1973), the...
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