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Volatility dynamics of the US business cycle: A multivariate asymmetric GARCH approach

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  • Ho, Kin-Yip
  • Tsui, Albert K.
  • Zhang, Zhaoyong

Abstract

Most empirical investigations of the business cycles in the United States have excluded the dimension of asymmetric conditional volatility. This paper analyses the volatility dynamics of the US business cycle by comparing the performance of various multivariate generalised autoregressive conditional heteroskedasticity (GARCH) models. In particular, we propose two bivariate GARCH models to examine the evidence of volatility asymmetry and time-varying correlations concurrently, and then apply the proposed models to five sectors of Industrial Production of the United States. Our findings provide strong evidence of asymmetric conditional volatility in all sectors, and some support of time-varying correlations in various sectoral pairs. This has important policy implications for government to consider the effective countercyclical measures during recessions.

Suggested Citation

  • Ho, Kin-Yip & Tsui, Albert K. & Zhang, Zhaoyong, 2009. "Volatility dynamics of the US business cycle: A multivariate asymmetric GARCH approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2856-2868.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:9:p:2856-2868
    DOI: 10.1016/j.matcom.2008.08.015
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    Cited by:

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    2. John Francis Diaz & Peh Ying Qian & Genevieve Liao Tan, 2018. "Variance Persistence in the Greater China Region: A Multivariate GARCH Approach," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 23(2), pages 49-68, July-Dec.
    3. Almeida, Pedro Cameira de & Fuinhas, José Alberto & Marques, António Cardoso, 2011. "A assimetria dos ciclos económicos: Evidência internacional usando o teste triples [The asymmetry of business cycles: International evidence using triples test]," MPRA Paper 35208, University Library of Munich, Germany.
    4. Khalfaoui, R & Boutahar, M, 2012. "Portfolio risk evaluation: An approach based on dynamic conditional correlations models and wavelet multiresolution analysis," MPRA Paper 41624, University Library of Munich, Germany.
    5. Parul Bhatia & Priya Gupta, 2020. "Sub-prime Crisis or COVID-19: A Comparative Analysis of Volatility in Indian Banking Sectoral Indices," FIIB Business Review, , vol. 9(4), pages 286-299, December.
    6. Tran, Thuy Nhung, 2022. "The Volatility of the Stock Market and Financial Cycle: GARCH Family Models," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 56(1), pages 151-168.
    7. John Francis T. Diaz, 2018. "Volatility Dynamics in the ASEAN– China Free Trade Agreement," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(3), pages 287-306, December.
    8. Mohammadi, H. & Abolhasani, L. & Shahnoushi, N. & Shabanian, F., 2018. "The effects of business cycle indicators on stock market indices of food industry in Iran," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277425, International Association of Agricultural Economists.
    9. Viorica CHIRILA, 2011. "The Modelling of the Volatility of Business cycles in Romania," EuroEconomica, Danubius University of Galati, issue 30, pages 138-147, November.

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    More about this item

    Keywords

    Constant correlations; US business cycle non-linearities; Index of industrial production; Multivariate asymmetric GARCH; Varying-correlations;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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