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Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach

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This paper applies the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low-frequency macroeconomic information in the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCH-MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle.

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  • 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.
    • Handle: RePEc:hhs:luwick:2013_004
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    1. Gabriel Perez‐Quiros & Allan Timmermann, 2000. "Firm Size and Cyclical Variations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(3), pages 1229-1262, June.
    2. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    4. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
    5. Whitelaw, Robert F, 1994. "Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns," Journal of Finance, American Finance Association, vol. 49(2), pages 515-541, June.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    8. Hamilton, James D & Gang, Lin, 1996. "Stock Market Volatility and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 573-593, Sept.-Oct.
    9. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    10. 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.
    11. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    12. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    13. Chernov, Mikhail & Ronald Gallant, A. & Ghysels, Eric & Tauchen, George, 2003. "Alternative models for stock price dynamics," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 225-257.
    14. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    15. Michael W. Brandt & Qiang Kang, 2002. "On the Relationship Between the Conditional Mean and Volatility of Stock Returns: A Latent VAR Approach," NBER Working Papers 9056, National Bureau of Economic Research, Inc.
    16. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    17. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    18. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    19. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    Cited by:

    1. Libing Fang & Baizhu Chen & Honghai Yu & Yichuo Qian, 2018. "The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 413-422, March.
    2. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2016. "Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 617-642.
    3. Yaya, OlaOluwa S. & Ogbonna, Ahamuefula E. & Adesina, Oluwaseun A. & Alobaloke, Kafayat A. & Vo, Xuan Vinh, 2022. "Time-variation between metal commodities and oil, and the impact of oil shocks: GARCH-MIDAS and DCC-MIDAS analyses," Resources Policy, Elsevier, vol. 79(C).
    4. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 111-122, January.
    5. Fang, Libing & Yu, Honghai & Xiao, Wen, 2018. "Forecasting gold futures market volatility using macroeconomic variables in the United States," Economic Modelling, Elsevier, vol. 72(C), pages 249-259.
    6. Ruobing Liu & Jianhui Yang & Chuan-Yang Ruan, 2019. "The Impact of Macroeconomic News on Chinese Futures," IJFS, MDPI, vol. 7(4), pages 1-14, October.
    7. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun, 2015. "Effects of macroeconomic uncertainty on the stock and bond markets," Finance Research Letters, Elsevier, vol. 13(C), pages 10-16.
    8. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Research Reports JRC84138, Joint Research Centre.
    9. Amit K. Sinha, 2021. "The reliability of geometric Brownian motion forecasts of S&P500 index values," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1444-1462, December.

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    More about this item

    Keywords

    Mixed data sampling; long-term variance component; macroeconomic variables; principal component; variance prediction.;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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