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The Growth-Volatility Relationship: New Evidence Based on Stochastic Volatility in Mean Models

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  • Lemoine, M.
  • Mougin, C.

Abstract

This paper models the relationship between growth and volatility for G7 economies in the time period 1960-2009. It delivers for the first time estimates of this relationship based on a logarithm variant of stochastic volatility in mean (SV-M) models. The relationship appears significantly positive in Germany and Italy, but insignificant in other countries. We also show that output volatility has increased in all countries since the beginning of the financial crisis, which illustrates the end of the great moderation. For comparison, the paper also delivers estimates based on a logarithm variant of GARCH in mean (log-GARCH-M) models, the class of time series models previously used in the literature to estimate the growth-volatility relationship. We show that SV-M models deliver results preferable to those of log-GARCH-M models, despite the high computational cost of their estimation. SV-M models fit generally better data than log-GARCH-M ones. As their residuals do not violate distribution assumptions, they do not deliver dubious conclusions concerning the significance of the relationship, which is the case of the log-GARCH-model for France, the UK and the US. Finally, SV-M models suggest a positive impact of unexpected volatility on output growth, which is not taken into account by log-GARCH-M models.

Suggested Citation

  • Lemoine, M. & Mougin, C., 2010. "The Growth-Volatility Relationship: New Evidence Based on Stochastic Volatility in Mean Models," Working papers 285, Banque de France.
  • Handle: RePEc:bfr:banfra:285
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    Cited by:

    1. Kuhanathan Ano Sujithan & Sanvi Avouyi-Dovi & Lyes Koliai, 2014. "On the determinants of food price volatility," Post-Print hal-01511900, HAL.
    2. Haroon Mumtaz & Francesco Zanetti, 2013. "The Impact of the Volatility of Monetary Policy Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(4), pages 535-558, June.
    3. Fuest, Angela & Schmidt, Torsten, 2020. "Inflation expectation uncertainty in a New Keynesian framework," Ruhr Economic Papers 867, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Haroon Mumtaz & Paolo Surico, 2018. "Policy uncertainty and aggregate fluctuations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 319-331, April.
    5. Haroon Mumtaz & Konstantinos Theodoridis, 2015. "The International Transmission Of Volatility Shocks: An Empirical Analysis," Journal of the European Economic Association, European Economic Association, vol. 13(3), pages 512-533, June.
    6. repec:dau:papers:123456789/12798 is not listed on IDEAS
    7. Haroon Mumtaz, 2016. "The Evolving Transmission of Uncertainty Shocks in the United Kingdom," Econometrics, MDPI, vol. 4(1), pages 1-18, March.
    8. Fuest, Angela & Schmidt, Torsten, 2017. "Inflation expectation uncertainty, inflation and the output gap," Ruhr Economic Papers 673, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Awijen, Haithem & Ben Zaied, Younes & Nguyen, Duc Khuong & Sensoy, Ahmet, 2020. "Endogenous Financial Uncertainty and Macroeconomic Volatility: Evidence from the United States," MPRA Paper 101276, University Library of Munich, Germany, revised Jun 2020.

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    More about this item

    Keywords

    Growth; Volatility; Sequential Monte-Carlo Methods.;
    All these keywords.

    JEL classification:

    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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