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Forecasting Forex Volatility In Turbulent Times

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  • Rajesh Mohnot

Abstract

The present study is an attempt to evaluate the predictability of the foreign exchange volatility in thirteen countries. The data covers the period of 2005-2009. To effectively forecast the volatility in the exchange rates, a GARCH model is used. The study compares the results between crisis period and a set of normal periods. The empirical results reveal that almost all countries except Thailand witnessed non-existence of volatility shocks at least once in a three year pre-crisis period but all the sample countries had volatility shocks in the crisis period of 2008-09. This apparently indicates that forecasting can be made at least for the next day given the high degree of volatility in the crisis period. The paper also reveals that exchange rates tend to have persistent conditional heteroskedasticity, and hence, could be predicted with one lag term.

Suggested Citation

  • Rajesh Mohnot, 2011. "Forecasting Forex Volatility In Turbulent Times," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 5(1), pages 27-38.
    • Handle: RePEc:ibf:gjbres:v:5:y:2011:i:1:p:27-38
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    References listed on IDEAS

    as
    1. Michael J. Sager & Mark P. Taylor, 2006. "Under the microscope: the structure of the foreign exchange market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(1), pages 81-95.
    2. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
    3. Michael Dooley & Rudi Dornbusch & Yung Chul Park, 2002. "A Framework for Exchange Rate Policy in Korea," Finance Working Papers 21757, East Asian Bureau of Economic Research.
    4. Chiara Pederzoli, 2006. "Stochastic Volatility and GARCH: a Comparison Based on UK Stock Data," The European Journal of Finance, Taylor & Francis Journals, vol. 12(1), pages 41-59.
    5. Morgan Aries & Gianfranco Giromini & Gunter Meissner, 2006. "A Model for a Fair Exchange Rate," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 51-66.
    6. Silvano Bordignon & Massimiliano Caporin & Francesco Lisi, 2009. "Periodic Long-Memory GARCH Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 60-82.
    7. Takezawa, Nobuya, 1995. "A note on intraday foreign exchange volatility and the informational role of quote arrivals," Economics Letters, Elsevier, vol. 48(3-4), pages 399-404, June.
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    Cited by:

    1. Gabriel Rodríguez & Junior A. Ojeda Cunya & José Carlos Gonzáles Tanaka, 2019. "An empirical note about estimation and forecasting Latin American Forex returns volatility: the role of long memory and random level shifts components," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 18(2), pages 107-123, June.

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    More about this item

    Keywords

    Forecasting; GARCH; Foreign exchange rates; Volatility; Financial Crisis;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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