IDEAS home Printed from https://ideas.repec.org/p/zbw/vfsc15/112919.html
   My bibliography  Save this paper

Misspecification Testing in GARCH-MIDAS Models

Author

Listed:
  • Conrad, Christian
  • Schienle, Melanie

Abstract

We develop a misspecification test for the multiplicative two-component GARCH-MIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly timevarying long-term component which is driven by the dynamics of a macroeconomic explanatory variable. We suggest a Lagrange Multiplier statistic for testing the null hypothesis that the macroeconomic variable has no explanatory power. Hence, under the null hypothesis the long-term component is constant and the GARCHMIDAS reduces to the simple GARCH model. We provide asymptotic theory for our test statistic and investigate its finite sample properties by Monte Carlo simulation. Our test statistic can be considered as an extension of the Lundbergh and Ter svirta (2002) ARCH nested in GARCH test for evaluating GARCH models. We illustrate the usefulness of our procedure by an empirical application to S&P 500 return data.

Suggested Citation

  • Conrad, Christian & Schienle, Melanie, 2015. "Misspecification Testing in GARCH-MIDAS Models," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112919, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc15:112919
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/112919/1/VfS_2015_pid_212.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cristina Amado & Timo Teräsvirta, 2017. "Specification and testing of multiplicative time-varying GARCH models with applications," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 421-446, April.
    2. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    3. Halunga, Andreea G. & Orme, Chris D., 2009. "First-Order Asymptotic Theory For Parametric Misspecification Tests Of Garch Models," Econometric Theory, Cambridge University Press, vol. 25(2), pages 364-410, April.
    4. Opschoor, Anne & Taylor, Nick & van der Wel, Michel & van Dijk, Dick, 2014. "Order flow and volatility: An empirical investigation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 185-201.
    5. Lundbergh, Stefan & Terasvirta, Timo, 2002. "Evaluating GARCH models," Journal of Econometrics, Elsevier, vol. 110(2), pages 417-435, October.
    6. Godfrey, Leslie G., 1978. "Testing for multiplicative heteroskedasticity," Journal of Econometrics, Elsevier, vol. 8(2), pages 227-236, October.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Conrad, Christian & Kleen, Onno, 2016. "On the statistical properties of multiplicative GARCH models," Working Papers 0613, University of Heidelberg, Department of Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian Conrad & Melanie Schienle, 2020. "Testing for an Omitted Multiplicative Long-Term Component in GARCH Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 229-242, April.
    2. Conrad, Christian & Schienle, Melanie, 2015. "Misspecification Testing in GARCH-MIDAS Models," Working Papers 0597, University of Heidelberg, Department of Economics.
    3. Christian Conrad & Robert F. Engle, 2021. "Modelling Volatility Cycles: The (MF)2 GARCH Model," Working Paper series 21-05, Rimini Centre for Economic Analysis.
    4. Cristina Amado & Annastiina Silvennoinen & Timo Teräsvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," CREATES Research Papers 2018-14, Department of Economics and Business Economics, Aarhus University.
    5. Chuffart Thomas & Flachaire Emmanuel & Péguin-Feissolle Anne, 2018. "Testing for misspecification in the short-run component of GARCH-type models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-17, December.
    6. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    7. Armin Pourkhanali & Jonathan Keith & Xibin Zhang, 2021. "Conditional Heteroscedasticity Models with Time-Varying Parameters: Estimation and Asymptotics," Monash Econometrics and Business Statistics Working Papers 15/21, Monash University, Department of Econometrics and Business Statistics.
    8. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    9. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    10. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    11. Campbell, John Y. & Giglio, Stefano & Polk, Christopher & Turley, Robert, 2018. "An intertemporal CAPM with stochastic volatility," Journal of Financial Economics, Elsevier, vol. 128(2), pages 207-233.
    12. Liu, Zhenhua & Zhang, Huiying & Ding, Zhihua & Lv, Tao & Wang, Xu & Wang, Deqing, 2022. "When are the effects of economic policy uncertainty on oil–stock correlations larger? Evidence from a regime-switching analysis," Economic Modelling, Elsevier, vol. 114(C).
    13. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    14. Udo, Eli A. & Obiora, Isitua K., 2006. "Determinants of Foreign Direct Investment and Economic Growth in the West African Monetary Zone: A System Equations Approach," Conference papers 331519, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    15. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    16. Russell, Bill & Chowdhury, Rosen Azad, 2013. "Estimating United States Phillips curves with expectations consistent with the statistical process of inflation," Journal of Macroeconomics, Elsevier, vol. 35(C), pages 24-38.
    17. Campos-Martins, Susana & Amado, Cristina, 2022. "Financial market linkages and the sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 123(C).
    18. Arthur J. Lin & Hai-Yen Chang, 2020. "Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    19. Dinghai Xu, 2021. "A study on volatility spurious almost integration effect: A threshold realized GARCH approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4104-4126, July.
    20. Dufour, Jean-Marie & Khalaf, Lynda & Bernard, Jean-Thomas & Genest, Ian, 2004. "Simulation-based finite-sample tests for heteroskedasticity and ARCH effects," Journal of Econometrics, Elsevier, vol. 122(2), pages 317-347, October.

    More about this item

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:vfsc15:112919. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vfsocea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.