Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Papers ; No. 2009-01

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods ; General Financial Markets: General (includes Measurement and Data)
• Subject: Composite Forecasts ; Forecast Evaluation ; GARCH ; Implied volatility ; Mexican Peso - U.S. Dollar Exchange Rate ; Regime-Switching ; Wechselkurs ; Volatilität ; Prognoseverfahren ; ARCH-Modell

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Benavides, Guillermo ; Capistrán, Carlos
• Event: Veröffentlichung
• (who): Banco de México
• (where): Ciudad de México
• (when): 2009

Further information

• Handle: http://hdl.handle.net/10419/83752
• Last update: 10.03.2025, 11:43 AM CET