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The (in)stability of stock returns and monetary policy interdependence in the US

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Abstract

We investigate the relationship between conventional monetary policy and stock market returns before, during, and after the zero lower bound (ZLB) period. Our inferential method, which exploits the exogenous changes in the variance of the structural shocks, allows us to recover both effects simultaneously without the need for restrictive identification assumptions. We find dramatic changes in the relationship between monetary policy and stock market returns over the period. Before the ZLB, policymakers reacted to stock returns. Their reaction has been muted since then. Regarding the stock market response, we find that, before the ZLB, a contractionary (expansionary) monetary policy reduces (increases) returns. Since the ZLB period, however, we cannot rule out a positive response of equity prices to monetary tightening.

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  • Emiliano A. Carlevaro & Leandro M. Magnusson, 2020. "The (in)stability of stock returns and monetary policy interdependence in the US," Economics Discussion / Working Papers 20-27, The University of Western Australia, Department of Economics.
    • Handle: RePEc:uwa:wpaper:20-27
    Note: MD5 = 8c7a220fc25386b30c59c2ffd9cfe7b6
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    1. 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.
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    More about this item

    Keywords

    Structural VAR; Identification; Instability; Monetary Policy;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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