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General to Specific Modelling of Exchange Rate Volatility : a Forecast Evaluation

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  • Luc, BAUWENS

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Department of Economics)

  • Genaro, SUCARRAT

Abstract

The general-to-specific (GETS) approach to modelling is widely employed in the modelling of economic series, but less so in financial volatility modelling due to computational complexity when many explanatory variables are involved. This study proposes a simple way of avoiding this problem and undertakes an out-of-sample forecast evaluation of the methodology applied to the modelling of weekly exchange rate volatility. Our findings suggest that GETS specifications are especially valuable in conditional forecasting, since the specification that employs actual values on the uncertain information performs particularly well.

Suggested Citation

  • Luc, BAUWENS & Genaro, SUCARRAT, 2006. "General to Specific Modelling of Exchange Rate Volatility : a Forecast Evaluation," Discussion Papers (ECON - Département des Sciences Economiques) 2006013, Université catholique de Louvain, Département des Sciences Economiques.
    • Handle: RePEc:ctl:louvec:2006013
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    References listed on IDEAS

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    1. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2005. "Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities," Econometrica, Econometric Society, vol. 73(1), pages 279-296, January.
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    Cited by:

    1. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2011. "Mortality density forecasts: An analysis of six stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 48(3), pages 355-367, May.
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    3. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
    4. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    5. Tennant, David, 2011. "Why do people risk exposure to Ponzi schemes? Econometric evidence from Jamaica," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(3), pages 328-346, July.
    6. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 3, pages 1-33.
    7. Genaro Sucarrat, 2010. "Econometric reduction theory and philosophy," Journal of Economic Methodology, Taylor & Francis Journals, vol. 17(1), pages 53-75.
    8. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2016. "Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 582-594.
    9. Carlisle E. Moody & Thomas B. Marvell, 2010. "On the Choice of Control Variables in the Crime Equation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 696-715, October.
    10. Kim Karlsson, Hyunjoo & Li, Yushu, 2024. "Investigation of Swedish krona exchange rate volatility by APARCH-Support Vector Regression," Working Papers in Economics and Statistics 10/2024, Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    11. Philipp Otto, 2022. "A Multivariate Spatial and Spatiotemporal ARCH Model," Papers 2204.12472, arXiv.org.
    12. Alvaro Escribano & Genaro Sucarrat, 2011. "Automated model selection in finance: General-to-speci c modelling of the mean and volatility speci cations," Working Papers 2011-09, Instituto Madrileño de Estudios Avanzados (IMDEA) Ciencias Sociales.
    13. 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.
    14. Genaro Sucarrat & Alvaro Escribano, 2012. "Automated Model Selection in Finance: General-to-Specific Modelling of the Mean and Volatility Specifications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(5), pages 716-735, October.
    15. Clavero, Borja, 2017. "A contribution to the Quantity Theory of Disaggregated Credit," MPRA Paper 76657, University Library of Munich, Germany.
    16. Panday, Anjan, 2015. "Impact of monetary policy on exchange market pressure: The case of Nepal," Journal of Asian Economics, Elsevier, vol. 37(C), pages 59-71.
    17. Sucarrat, Genaro & Escribano, Álvaro, 2010. "The power log-GARCH model," UC3M Working papers. Economics we1013, Universidad Carlos III de Madrid. Departamento de Economía.
    18. J. James Reade & Ulrich Volz, 2011. "From the General to the Specific," Discussion Papers 11-18, Department of Economics, University of Birmingham.
    19. Sucarrat, Genaro & Escribano, Álvaro, 2009. "Automated financial multi-path GETS modelling," UC3M Working papers. Economics we093620, Universidad Carlos III de Madrid. Departamento de Economía.
    20. Lyonnet, Victor & Werner, Richard, 2012. "Lessons from the Bank of England on ‘quantitative easing’ and other ‘unconventional’ monetary policies," International Review of Financial Analysis, Elsevier, vol. 25(C), pages 94-105.
    21. Cui, Jin & In, Francis & Maharaj, Elizabeth Ann, 2016. "What drives the Libor–OIS spread? Evidence from five major currency Libor–OIS spreads," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 358-375.

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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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