International Economics and Economic Policy

, Volume 12, Issue 1, pp 127–142 | Cite as

Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts

Original Paper

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.

Keywords

Exchange Rate Volatility modelling 

JEL classification

E44 G12 

References

  1. Andersen T, Bollerslev T (1998) DM-Dollar volatility: intraday activity patterns, macroeconomic announcements, and longer-run dependencies. J Financ 53(1):219–265CrossRefGoogle Scholar
  2. Andersen T, Bollerslev T, Diebold FA, Labys P (2001) The distribution of realized exchange rate volatility. J Am Stat Assoc 96(453):42–55CrossRefGoogle Scholar
  3. Andersen T, Bollerslev T, Diebold FX, Labys P (2003) Modeling and forecasting realized volatility. Econometrica 71(2):579–626CrossRefGoogle Scholar
  4. Baillie RA, Bollerslev T (1991) Intra-day and inter-market volatility in foreign exchange rates. Rev Econ Stud 58(3):565–585CrossRefGoogle Scholar
  5. Balaban E (2004) Comparative forecasting performance of symmetric and asymmetric conditional volatility models of an exchange rate. Econ Lett 83(1):99–105CrossRefGoogle Scholar
  6. Bildirici M, Ersin O (2009) Improving forecasts of GARCH family models with the artificial neural networks: an application to the daily returns in Istanbul stock exchange. Expert Syst Appl 36(4):7355–7362CrossRefGoogle Scholar
  7. Bollerslev T (1986) Generalized autoregressive conditional heteroscedasticity. J Econ 31(3):307–327CrossRefGoogle Scholar
  8. Bollerslev T (2008) A Glossary of ARCH (GARCH), Creates Research Paper, 2008–49Google Scholar
  9. Broll U, Hansen-Averlant S (2010) Exchange rate volatility, international trade and labour demand. IEEP 7(4):423–36CrossRefGoogle Scholar
  10. Brownlees C, Gallo M (2010) Comparison of volatility measures: a risk management perspective. J Financ Econ 8(1):29–56Google Scholar
  11. Busch T, Christensen B, Neilsen M (2012) The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. J Econ 160(1):48–57CrossRefGoogle Scholar
  12. Chen X, Ghysels E, Wang F (2011) HYBRID GARCH models and intra-daily return periodicity. J Time Ser Econ 3(1):1–28CrossRefGoogle Scholar
  13. Donaldson R, Kamstra M (2005) Volatility forecasts, trading volume and the ARCH vs option implied volatility tradeoff. J Financ Res 27(4):519–538CrossRefGoogle Scholar
  14. Dunis C, Laws J, Chauvin S (2003) FX volatility forecasts and the informational data for volatility. Eur J Financ 17(1):117–160Google Scholar
  15. Engle R, Patton A (2001) What is a good volatility model? Quant Financ 1(2):237–245CrossRefGoogle Scholar
  16. Ghysels E, Santa-Clara P, Valkanov R (2005) Predicting volatility: getting the most out of return sampled at different frequencies. J Econ 131(1):59–95Google Scholar
  17. Glosten L, Jagannathan R, Runkle D (1993) On the relation between expected value and the volatility of the nominal excess return on stocks. J Financ 48(5):1779–1801CrossRefGoogle Scholar
  18. Hansen P, Lunde X (2005) A forecast comparison of volatility models: does anything beat a GARCH(1,1). J Appl Econ 20(7):873–889CrossRefGoogle Scholar
  19. Higgins M, Bera A (1992) A class of nonlinear ARCH models. Int Econ Rev 33(1):137–158CrossRefGoogle Scholar
  20. Neely C (2009) Forecasting foreign exchange volatility: why is implied volatility biased and inefficient? and does it matter? J Int Financ Mark Inst Money 19(1):188–205CrossRefGoogle Scholar
  21. Nelson (1991) ‘Conditional heteroskedasticity in asset returns: a new approach’. Econometrica 59(2):347–370CrossRefGoogle Scholar
  22. Ranaldo A (2008) Segmentation and time-of-day patterns in the foreign exchange market. J Bank Financ 33(12):2199–2206CrossRefGoogle Scholar
  23. Sentana E (1998) Quadratic ARCH Models. The Review of Economic Studies 62(4):639–661Google Scholar
  24. Silvennoinen A, Terasvirta T (2008) Multivariate GARCH models. In: Andersen TG, Davis, Kreiss, Mikosch (eds) Handbook of financial time series. Springer, New YorkGoogle Scholar
  25. Taylor S (1986) Modelling financial time series. Wiley, ChichesterGoogle Scholar
  26. Zakoïan J (1994) Threshold heteroskedastic models. J Econ Dyn Control 18(5):931–955CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.City University LondonLondonUK

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