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Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets

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  • Alper, C. Emre
  • Fendoglu, Salih
  • Saltoglu, Burak

Abstract

We explore the relative weekly stock market volatility forecasting performance of the linear univariate MIDAS regression model based on squared daily returns vis-a-vis the benchmark model of GARCH(1,1) for a set of four developed and ten emerging market economies. We first estimate the two models for the 2002-2007 period and compare their in-sample properties. Next we estimate the two models using the data on 2002-2005 period and then compare their out-of-sample forecasting performance for the 2006-2007 period, based on the corresponding mean squared prediction errors following the testing procedure suggested by West (2006). Our findings show that the MIDAS squared daily return regression model outperforms the GARCH model significantly in four of the emerging markets. Moreover, the GARCH model fails to outperform the MIDAS regression model in any of the emerging markets significantly. The results are slightly less conclusive for the developed economies. These results may imply superior performance of MIDAS in relatively more volatile environments.

Suggested Citation

  • Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:7460
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    References listed on IDEAS

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    Cited by:

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    2. Afees A. Salisu & Umar B. Ndako & Idris Adediran, 2018. "Forecasting GDP of OPEC: The role of oil price," Working Papers 044, Centre for Econometric and Allied Research, University of Ibadan.
    3. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    4. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    5. Biswas, Anindya, 2014. "The output gap and expected security returns," Review of Financial Economics, Elsevier, vol. 23(3), pages 131-140.
    6. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    7. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    8. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach," Working Papers 025, Centre for Econometric and Allied Research, University of Ibadan.
    9. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    10. Jennie Bai & Eric Ghysels & Jonathan H. Wright, 2013. "State Space Models and MIDAS Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 32(7), pages 779-813, October.
    11. Chan-Guk Huh & Jie Wu, 2015. "Linkage between US monetary policy and emerging economies: the case of Korea?s financial market and monetary policy," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 4(3), pages 1-18, September.
    12. Julián Alonso Cárdenas-Cárdenas & Edgar Caicedo-García & Eliana R. González Molano, 2020. "Estimación de la variación del precio de los alimentos con modelos de frecuencias mixtas," Borradores de Economia 1109, Banco de la Republica de Colombia.
    13. Anindya Biswas, 2014. "The output gap and expected security returns," Review of Financial Economics, John Wiley & Sons, vol. 23(3), pages 131-140, September.
    14. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    15. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    16. Asgharian, Hossein & Hou, Ai Jun & Javed, Farrukh, 2013. "Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach," Knut Wicksell Working Paper Series 2013/4, Lund University, Knut Wicksell Centre for Financial Studies.

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    More about this item

    Keywords

    Mixed Data Sampling regression model; Conditional volatility forecasting; Emerging Markets;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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