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Structural breaks and GARCH models of exchange rate volatility: Re‐examination and extension

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  • Akram Shavkatovich Hasanov
  • Robert Brooks
  • Sirojiddin Abrorov
  • Aktam Usmanovich Burkhanov

Abstract

We examine the empirical significance of structural changes concerning generalized autoregressive conditional heteroskedasticity (GARCH) models of exchange rate volatility using out‐of‐sample tests by replicating and carrying out robustness checks on the volatility forecasting study by Rapach and Strauss (Journal of Applied Econometrics, 2008; 23, 65–90). We employ the same econometric models but incorporate recent US dollar daily exchange rates data while also using different software, a relatively recent forecast accuracy test and loss metrics. Our objective is to attain scientific replication in a broad sense. Our analysis verifies and broadly aligns with the results obtained in the original study. In particular, we find strong evidence that the models incorporating structural breaks demonstrate superior performance across all loss functions and forecast horizons compared with those models that ignore instabilities.

Suggested Citation

  • Akram Shavkatovich Hasanov & Robert Brooks & Sirojiddin Abrorov & Aktam Usmanovich Burkhanov, 2024. "Structural breaks and GARCH models of exchange rate volatility: Re‐examination and extension," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1403-1407, November.
    • Handle: RePEc:wly:japmet:v:39:y:2024:i:7:p:1403-1407
    DOI: 10.1002/jae.3091
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    References listed on IDEAS

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    1. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    2. Mueller-Langer, Frank & Fecher, Benedikt & Harhoff, Dietmar & Wagner, Gert G., 2019. "Replication studies in economics—How many and which papers are chosen for replication, and why?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 48(1), pages 62-83.
    3. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    4. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    5. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-234, April.
    6. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    7. Philippe Mueller & Alireza Tahbaz-Salehi & Andrea Vedolin, 2017. "Exchange Rates and Monetary Policy Uncertainty," Journal of Finance, American Finance Association, vol. 72(3), pages 1213-1252, June.
    8. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    9. Peter Reinhard Hansen & Asger Lunde & Valeri Voev, 2014. "Realized Beta Garch: A Multivariate Garch Model With Realized Measures Of Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 774-799, August.
    10. Kurov, Alexander & Stan, Raluca, 2018. "Monetary policy uncertainty and the market reaction to macroeconomic news," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 127-142.
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