IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/111482.html
   My bibliography  Save this paper

Explaining the nature of economic volatility based on GDP and international trade: a study on China and the United States

Author

Listed:
  • Saha, Mallika
  • Dutta, Kumar Debasis
  • Islam, MD. Shafiqul

Abstract

Economic volatility refers to the dispersion of an economic variable, especially the output growth, from its expected value, which has an immense impact on the livelihoods of many people and thus, regarded as one of the most important research topics of economic discourse. Gross domestic product (GDP) and international trade are two key indicators of a nation’s economy that measure total economic activity and activities across borders, respectively. Hence to explain the economic volatility, this study aims to investigate its nature in terms of GDP and international trade of the two largest economy of world, China and the United States, from 1993 to 2018 using different ARCH-type models (GARCH, EGARCH and TGARCH). According to the fndings, the TGARCH and EGARCH exhibit the best statistical ft and the asymmetric parameters of the models are signifcant for almost all the variables. Therefore, this study establishes that economic volatility in terms of real GDP growth and international trade (export and import) is asymmetric.

Suggested Citation

  • Saha, Mallika & Dutta, Kumar Debasis & Islam, MD. Shafiqul, 2020. "Explaining the nature of economic volatility based on GDP and international trade: a study on China and the United States," MPRA Paper 111482, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:111482
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/111482/1/MPRA_paper_111482.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamori, Shigeyuki, 2000. "Volatility of real GDP: some evidence from the United States, the United Kingdom and Japan," Japan and the World Economy, Elsevier, vol. 12(2), pages 143-152, May.
    2. Julian di Giovanni & Andrei A. Levchenko, 2012. "The Risk Content of Exports: A Portfolio View of International Trade," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 8(1), pages 97-151.
    3. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Ho, Kin-Yip & Tsui, Albert K. C., 2003. "Asymmetric volatility of real GDP: some evidence from Canada, Japan, the United Kingdom and the United States," Japan and the World Economy, Elsevier, vol. 15(4), pages 437-445, December.
    6. Frederick van der Ploeg & Steven Poelhekke, 2009. "Volatility and the natural resource curse," Oxford Economic Papers, Oxford University Press, vol. 61(4), pages 727-760, October.
    7. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    8. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    9. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    10. Stilianos Fountas & Menelaos Karanasos & Alfonso Mendoza, 2004. "Output Variability and Economic Growth: the Japanese Case," Bulletin of Economic Research, Wiley Blackwell, vol. 56(4), pages 353-363, October.
    11. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    12. Joël CARIOLLE, 2012. "Measuring macroeconomic volatility - Applications to export revenue data, 1970-2005," Working Papers I14, FERDI.
    Full references (including those not matched with items on IDEAS)

    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. Hamori, Shigeyuki, 2000. "Volatility of real GDP: some evidence from the United States, the United Kingdom and Japan," Japan and the World Economy, Elsevier, vol. 12(2), pages 143-152, May.
    2. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2021. "The impact of Euro through time: Exchange rate dynamics under different regimes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1375-1408, January.
    3. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    4. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
    5. Syed Kamran Ali Haider & Shujahat Haider Hashmi & Ishtiaq Ahmed, 2017. "Systematic Risk Factors And Stock Return Volatility," APSTRACT: Applied Studies in Agribusiness and Commerce, AGRIMBA, vol. 11(1-2), September.
    6. Rockinger, M. & Jondeau, E., 2001. "Conditional Dependency of Financial Series: An Application of Copulas," Working papers 82, Banque de France.
    7. Tim Bollerslev & Robert J. Hodrick, 1992. "Financial Market Efficiency Tests," NBER Working Papers 4108, National Bureau of Economic Research, Inc.
    8. Wu, Guojun & Xiao, Zhijie, 2002. "A generalized partially linear model of asymmetric volatility," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 287-319, August.
    9. Li, Kui-Wai & Kwok, Ming-Lok, 2009. "Output volatility of five crisis-affected East Asia economies," Japan and the World Economy, Elsevier, vol. 21(2), pages 172-182, March.
    10. LUBRANO, Michel, 1998. "Smooth transition GARCH models: a Bayesian perspective," LIDAM Discussion Papers CORE 1998066, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Fang, WenShwo & Miller, Stephen M., 2009. "Modeling the volatility of real GDP growth: The case of Japan revisited," Japan and the World Economy, Elsevier, vol. 21(3), pages 312-324, August.
    12. Kin-Yip Ho & Albert K. Tsui & Zhaoyong Zhang, 2009. "Volatility Dynamics of the UK Business Cycle: a Multivariate Asymmetric Garch Approach," Economie Internationale, CEPII research center, issue 117, pages 31-46.
    13. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    14. El Jebari, Ouael & Hakmaoui, Abdelati, 2018. "GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 237-249, Diciembre.
    15. Perry Sadorsky & Michael D. McKenzie, 2008. "Power transformation models and volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 587-606.
    16. Shekar Bose & Hafizur Rahman, 2022. "Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market," SAGE Open, , vol. 12(4), pages 21582440221, October.
    17. Stelios Arvanitis & Antonis Demos, 2004. "Time Dependence and Moments of a Family of Time‐Varying Parameter Garch in Mean Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(1), pages 1-25, January.
    18. Avouyi-Dovi, S. & Jondeau, E., 1999. "Interest Rate Transmission and Volatility Transmission along the Yield Curve," Working papers 57, Banque de France.
    19. Li, Ming-Yuan Leon, 2008. "Clarifying the dynamics of the relationship between option and stock markets using the threshold vector error correction model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 511-520.
    20. Y. K. Tse, 2002. "Residual-based diagnostics for conditional heteroscedasticity models," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 358-374, June.

    More about this item

    Keywords

    Economic volatility; China; USA; GARCH; EGARCH; TGARCH;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:pra:mprapa:111482. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.