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Forecasting Exchange Rate Volatility: The Superior Performance of Conditional Combinations of Time Series and Option Implied Forecasts

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  • Benavides Guillermo
  • Capistrán Carlos

Abstract

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 intra-day data is used as a proxy for the (latent) daily volatility.

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  • Benavides Guillermo & Capistrán Carlos, 2009. "Forecasting Exchange Rate Volatility: The Superior Performance of Conditional Combinations of Time Series and Option Implied Forecasts," Working Papers 2009-01, Banco de México.
    • Handle: RePEc:bdm:wpaper:2009-01
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    Cited by:

    1. Holger Fink & Andreas Fuest & Henry Port, 2018. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates," Risks, MDPI, vol. 6(3), pages 1-19, August.
    2. Bush, Georgia & López Noria, Gabriela, 2021. "Uncertainty and exchange rate volatility: Evidence from Mexico," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 704-722.
    3. Dimitrios I. Vortelinos, 2015. "Out‐of‐sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini‐futures markets," Review of Financial Economics, John Wiley & Sons, vol. 27(1), pages 58-67, November.
    4. Gustavo Cabrera González, 2019. "Modeling and Projection of the Mexican Exchange Rate (Peso/Dollar): a Bayesian Approach for Model Selection," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(2), pages 203-219, Abril-Jun.
    5. Salisu, Afees A. & Cuñado, Juncal & Gupta, Rangan, 2022. "Geopolitical risks and historical exchange rate volatility of the BRICS," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 179-190.
    6. Luo, Xingguo & Ye, Zinan, 2015. "Predicting volatility of the Shanghai silver futures market: What is the role of the U.S. options market?," Finance Research Letters, Elsevier, vol. 15(C), pages 68-77.
    7. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
    8. Abarca Gustavo & Rangel José Gonzalo & Benavides Guillermo, 2010. "Exchange Rate Market Expectations and Central Bank Policy: The case of the Mexican Peso-US Dollar from 2005-2009," Working Papers 2010-17, Banco de México.
    9. Gozgor, Giray & Nokay, Pinar, 2011. "Comparing forecast performances among volatility estimation methods in the pricing of european type currency options of USD-TL and Euro-TL," MPRA Paper 34369, University Library of Munich, Germany.
    10. Guillermo Benavides, 2011. "Central Bank Exchange Rate Interventions and Market Expectations: The Case of México During the Financial Crisis 2008-2009," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 6(1), pages 5-27, Julio-Dic.
    11. Linlan Xiao & Vigdis Boasson & Sergey Shishlenin & Victoria Makushina, 2018. "Volatility forecasting: combinations of realized volatility measures and forecasting models," Applied Economics, Taylor & Francis Journals, vol. 50(13), pages 1428-1441, March.
    12. Vortelinos, Dimitrios I., 2015. "Out-of-sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini-futures markets," Review of Financial Economics, Elsevier, vol. 27(C), pages 58-67.
    13. Guillermo Benavides Perales, 2012. "Central Bank Exchange Rate Interventions and Market Expectations: The Case of México During the Financial Crisis 2008-2009," Remef - The Mexican Journal of Economics and Finance, Instituto Mexicano de Ejecutivos de Finanzas. Remef, October.
    14. Slim, Skander & Dahmene, Meriam & Boughrara, Adel, 2020. "How informative are variance risk premium and implied volatility for Value-at-Risk prediction? International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 22-37.
    15. López Noria Gabriela & Bush Georgia, 2019. "Uncertainty and Exchange Rate Volatility: the Case of Mexico," Working Papers 2019-12, Banco de México.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; 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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