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Could Markets Have Helped Predict the Puzzling Exchange Rate Path in CESEE Countries during the Current Crisis?

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  • Jesús Crespo Cuaresma

    (Vienna University of Economics and Business, Institute for Fiscal and Monetary Policy)

  • Tomáš Slacík

    (Oesterreichische Nationalbank, Foreign Research Division)

Abstract

In the present paper we examine whether financial markets could have helped predict exchange rates in selected Central, Eastern and Southeastern European (CESEE) economies, namely the Czech Republic, Hungary and Poland, during the current financial crisis. To this end, we derive risk-neutral densities from the implied volatilities of FX options, which approximate market expectations about exchange rate developments. Based on these risk-neutral density estimates, we then assess the out-of-sample predictive power of indicators. The forecasting results suggest that models based on FX options are inferior to the random walk in terms of the forecasting error, confirming a stylized fact about the short-term forecasting of exchange rates. Yet, we also find that, for the Czech Republic and Poland, risk-neutral densities contain useful information on the direction of change of the exchange rate.

Suggested Citation

  • Jesús Crespo Cuaresma & Tomáš Slacík, 2010. "Could Markets Have Helped Predict the Puzzling Exchange Rate Path in CESEE Countries during the Current Crisis?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 1, pages 32-48.
    • Handle: RePEc:onb:oenbfi:y:2010:i:1:b:2
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    References listed on IDEAS

    as
    1. Jesús Crespo Cuaresma & Martin Feldkircher, 2013. "Spatial Filtering, Model Uncertainty And The Speed Of Income Convergence In Europe," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 720-741, June.
    2. Pavel Bouc & Martin Cincibuch, 2004. "An Interpretation of Czech FX Options," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 54(7-8), pages 286-304, July.
    3. Crespo Cuaresma, Jesús & Slacik, Tomas, 2009. "On the determinants of currency crises: The role of model uncertainty," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 621-632, December.
    4. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    5. Jesus Crespo Cuaresma, "undated". "Forecasting euro exchange rates: How much does model averaging help?," Working Papers 2007-24, Faculty of Economics and Statistics, Universität Innsbruck.
    6. Breeden, Douglas T & Litzenberger, Robert H, 1978. "Prices of State-contingent Claims Implicit in Option Prices," The Journal of Business, University of Chicago Press, vol. 51(4), pages 621-651, October.
    7. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    8. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    9. Garman, Mark B. & Kohlhagen, Steven W., 1983. "Foreign currency option values," Journal of International Money and Finance, Elsevier, vol. 2(3), pages 231-237, December.
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    Cited by:

    1. Geršl, Adam & Lešanovská, Jitka, 2014. "Explaining the Czech interbank market risk premium," Economic Systems, Elsevier, vol. 38(4), pages 536-551.

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    More about this item

    Keywords

    Options; implied volatility; risk-neutral density; exchange rate forecasting; Bayesian model averaging; subprime crisis; emerging markets;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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