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Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts

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  • Keith Pilbeam
  • Kjell Langeland

Abstract

This study investigates whether different specifications of univariate GARCH models can usefully forecast volatility in the foreign exchange market. The study compares in-sample forecasts from symmetric and asymmetric GARCH models with the implied volatility derived from currency options for four dollar parities. The data set covers the period 2002 to 2012. We divide the data into two periods one for the period 2002 to 2007 which is characterised by low volatility and the other for the period 2008 to 2012 characterised by high volatility. The results of this paper reveal that the implied volatility forecasts significantly outperform the three GARCH models in both low and high volatility periods. The results strongly suggest that the foreign exchange market efficiently prices in future volatility. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Keith Pilbeam & Kjell Langeland, 2015. "Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts," International Economics and Economic Policy, Springer, vol. 12(1), pages 127-142, March.
    • Handle: RePEc:kap:iecepo:v:12:y:2015:i:1:p:127-142
    DOI: 10.1007/s10368-014-0289-4
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    2. Andrew Phiri, 2018. "Nonlinear Relationship between Exchange Rate Volatility and Economic Growth," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 61(3), pages 15-38.
    3. Fourie, Justin & Pretorius, Theuns & Harvey, Rhett & Henrico, Van Niekerk & Phiri, Andrew, 2016. "Nonlinear relationship between exchange rate volatility and economic growth: A South African perspective," MPRA Paper 74671, University Library of Munich, Germany.
    4. Allison Roehling, 2021. "Implications of exchange rate volatility for trade: Volatility measurement matters," Review of International Economics, Wiley Blackwell, vol. 29(5), pages 1486-1523, November.
    5. Dicle, Mehmet F. & Levendis, John, 2020. "Historic risk and implied volatility," Global Finance Journal, Elsevier, vol. 45(C).
    6. Emmanuel Afuecheta & Idika E. Okorie & Saralees Nadarajah & Geraldine E. Nzeribe, 2024. "Forecasting Value at Risk and Expected Shortfall of Foreign Exchange Rate Volatility of Major African Currencies via GARCH and Dynamic Conditional Correlation Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 271-304, January.
    7. Christian Bucio-Pacheco & Miriam Sosa-Castro & Francisco Reyes-Zarate, 2023. "Volatilidad dinamica en el sector bancario en Mexico: evidencia DCC-GARCH vs Copula-GARCH," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., vol. 20(2), pages 69-93, Julio-Dic.
    8. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    9. Muhammad Ahsanuddin & Tayyab Raza Fraz & Samreen Fatima, 2019. "Studying the Volatility of Pakistan Stock Exchange and Shanghai Stock Exchange Markets in the Light of CPEC: An Application of GARCH and EGARCH Modelling," International Journal of Sciences, Office ijSciences, vol. 8(03), pages 125-132, March.
    10. Meng, Juan & Nie, He & Mo, Bin & Jiang, Yonghong, 2020. "Risk spillover effects from global crude oil market to China’s commodity sectors," Energy, Elsevier, vol. 202(C).
    11. Ruipeng Liu & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2017. "Do Bivariate Multifractal Models Improve Volatility Forecasting in Financial Time Series? An Application to Foreign Exchange and Stock Markets," Working Papers 201728, University of Pretoria, Department of Economics.
    12. Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023. "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 285-305, June.
    13. Liu, Dehong & Liang, Yucong & Zhang, Lili & Lung, Peter & Ullah, Rizwan, 2021. "Implied volatility forecast and option trading strategy," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 943-954.
    14. Khyati Kathuria & Nand Kumar, 2022. "Pandemic‐induced fear and government policy response as a measure of uncertainty in the foreign exchange market: Evidence from (a)symmetric wild bootstrap likelihood ratio test," Pacific Economic Review, Wiley Blackwell, vol. 27(4), pages 361-379, October.
    15. Havva Koc, 2021. "Exchange Rate Volatility in the Covid-19 Period: An Analysis Using the Markov-Switching ARCH Model," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(35), pages 205-220, December.
    16. Narayan Tondapu, 2024. "Analyzing Currency Fluctuations: A Comparative Study of GARCH, EWMA, and IV Models for GBP/USD and EUR/GBP Pairs," Papers 2402.07435, arXiv.org.
    17. Zhou, Zhongbao & Fu, Zhangyan & Jiang, Yong & Zeng, Ximei & Lin, Ling, 2020. "Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 34(C).
    18. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2018. "Forecasting (Good and Bad) Realized Exchange-Rate Volatility: Is there a Role for Realized Skewness and Kurtosis?," Working Papers 201879, University of Pretoria, Department of Economics.
    19. Ganbold, Batzorig & Akram, Iqra & Fahrozi Lubis, Raisal, 2017. "Exchange rate volatility: A forecasting approach of using the ARCH family along with ARIMA SARIMA and semi-structural-SVAR in Turkey," MPRA Paper 84447, University Library of Munich, Germany, revised 2017.
    20. Idil Uz Akdogan, 2023. "Monetary policy responses to COVID-19 in emerging European economies: measuring the QE announcement effects on foreign exchange markets," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(3), pages 625-655, August.
    21. Saker Sabkha & Christian de Peretti & Dorra Hmaied, 2018. "Forecasting sovereign CDS volatility: A comparison of univariate GARCH-class models," Working Papers hal-01769390, HAL.

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    More about this item

    Keywords

    Exchange Rate; Volatility modelling; E44; G12;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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