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Forecasting Volatility in Developing Countries' Nominal Exchange Returns

Author

Listed:
  • Antonakakis, Nikolaos
  • Darby, Julia
Abstract
This paper identifies the best models for forecasting the volatility of daily exchange returns of developing countries. An emerging consensus in the recent literature focusing on industrialised counties has noted the superior performance of the FIGARCH model in the case of industrialised countries, a result that is reaffirmed here. However, we show that when dealing with developing countries’ data the IGARCH model results in substantial gains in terms of the in-sample results and out-of-sample forecasting performance.

Suggested Citation

  • Antonakakis, Nikolaos & Darby, Julia, 2012. "Forecasting Volatility in Developing Countries' Nominal Exchange Returns," MPRA Paper 40875, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:40875
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    References listed on IDEAS

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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.
    2. Victor Shevchuk & Roman Kopych, 2021. "Exchange Rate Volatility, Currency Misalignment, and Risk of Recession in the Central and Eastern European Countries," Risks, MDPI, vol. 9(5), pages 1-19, May.
    3. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    4. Lucey, Brian & Xiaoxue, Wang & Yanfang, Wang & Ying, Xu, 2020. "Can financial marketization mitigate the negative effect of exchange rate fluctuations on exports? Evidence from Chinese regions," Finance Research Letters, Elsevier, vol. 34(C).
    5. Dritsaki, Chaido, 2019. "Modeling the Volatility of Exchange Rate Currency using GARCH Model," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 72(2), pages 209-230.
    6. Ouael EL JEBARI & Abdelati HAKMAOUI, 2017. "Modeling persistence of volatility in the Moroccan exchange market using a fractionally integrated EGARCH," Turkish Economic Review, KSP Journals, vol. 4(4), pages 388-399, December.
    7. Sherif, Mohamed, 2020. "The impact of Coronavirus (COVID-19) outbreak on faith-based investments: An original analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).

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    More about this item

    Keywords

    Exchange rate volatility; estimation; forecasting; developing countries;
    All these keywords.

    JEL classification:

    • 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
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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