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Short-Run and Long-Run Causality between Monetary Policy Variables and Stock Prices

Author

Listed:
  • Jean-Marie Dufour
  • David Tessier

Abstract

The authors examine simultaneously the causal links connecting monetary policy variables, real activity, and stock returns. Their interest lies in the fact that the dynamics of asset prices can provide key insights--in terms of information--for the conduct of monetary policy, since asset prices constitute a class of potentially leading indicators of either economic activity or inflation. This is of particular interest in the context of an inflation-targeting regime, where the monetary policy stance is set according to inflation forecasts. While most empirical studies on causality have examined this issue using Granger's (1969) original definition, the authors examine the causality relations through the generalization proposed in Dufour and Renault (1998). For the United States, the authors find no support for stock returns as a leading indicator of the macroeconomic variables considered, or for stock returns being influenced by those macroeconomic variables, except for one case: fluctuations in M1 tend to anticipate fluctuations in stock returns. Furthermore, the authors' empirical methodology allows them to infer that monetary aggregates may have significant predictive power for income and prices at longer horizons. It is therefore incorrect to dismiss the importance of monetary aggregates based on the usual Granger causality criteria. The causality pattern inferred by the authors' procedure is consistent with the Phillips curve (for the inflation dynamics) and with the Taylor rule in the case of the interest rate. For Canada, the results are much different. The authors show that there is a potential role for asset prices as a predictor of some important macroeconomic variables, namely interest rates, inflation, and output at policy-relevant horizons. Furthermore, some measures of monetary aggregates tend to dominate the interest rate as robust causal variables for output growth and inflation. However, the authors do not find strong evidence in favour of the Phillips curve and the Taylor rule. Finally, for both Canada and the United States, the authors show that seasonal adjustments can highly distort the inferred causality structure.

Suggested Citation

  • Jean-Marie Dufour & David Tessier, 2006. "Short-Run and Long-Run Causality between Monetary Policy Variables and Stock Prices," Staff Working Papers 06-39, Bank of Canada.
  • Handle: RePEc:bca:bocawp:06-39
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    References listed on IDEAS

    as
    1. Charles Bean, 2003. "Asset Prices, Financial Imbalances and Monetary Policy: Are Inflation Targets Enough?," RBA Annual Conference Volume,in: Anthony Richards & Tim Robinson (ed.), Asset Prices and Monetary Policy Reserve Bank of Australia.
    2. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, pages 443-477.
    3. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    4. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    5. Stephen G. Cecchetti & Hans Genberg & Sushil Wadhwani, 2002. "Asset Prices in a Flexible Inflation Targeting Framework," NBER Working Papers 8970, National Bureau of Economic Research, Inc.
    6. Sims, Christopher A, 1980. "Comparison of Interwar and Postwar Business Cycles: Monetarism Reconsidered," American Economic Review, American Economic Association, pages 250-257.
    7. EHLERS, Lars, 2003. "In Search of Advice for Physicians in Entry-Level Medical Markets," Cahiers de recherche 2003-15, Universite de Montreal, Departement de sciences economiques.
    8. Dufour, Jean-Marie & Pelletier, Denis & Renault, Eric, 2006. "Short run and long run causality in time series: inference," Journal of Econometrics, Elsevier, pages 337-362.
    9. Benchekroun, Hassan, 2008. "Comparative dynamics in a productive asset oligopoly," Journal of Economic Theory, Elsevier, pages 237-261.
    10. Dufour, Jean-Marie & Pelletier, Denis & Renault, Eric, 2006. "Short run and long run causality in time series: inference," Journal of Econometrics, Elsevier, pages 337-362.
    11. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, pages 788-829.
    12. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, pages 443-477.
    13. Jean-Marie Dufour & Eric Renault, 1998. "Short Run and Long Run Causality in Time Series: Theory," Econometrica, Econometric Society, pages 1099-1126.
    14. Lee, Bong-Soo, 1992. " Causal Relations among Stock Returns, Interest Rates, Real Activity, and Inflation," Journal of Finance, American Finance Association, vol. 47(4), pages 1591-1603, September.
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    Citations

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    Cited by:

    1. Lee, Tae-Hwy & Yang, Weiping, 2014. "Granger-causality in quantiles between financial markets: Using copula approach," International Review of Financial Analysis, Elsevier, pages 70-78.
    2. Woźniak, Tomasz, 2015. "Testing causality between two vectors in multivariate GARCH models," International Journal of Forecasting, Elsevier, pages 876-894.
    3. Matthieu Droumaguet & Anders Warne & Tomasz Wozniak, 2015. "Granger Causality and Regime Inference in Bayesian Markov-Switching VARs," Department of Economics - Working Papers Series 1191, The University of Melbourne.
    4. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    5. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, pages 207-230.
    6. Majid M. Al-Sadoon, 2015. "Testing subspace Granger causality," Economics Working Papers 1495, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Li, Yun Daisy & Iscan, Talan B. & Xu, Kuan, 2010. "The impact of monetary policy shocks on stock prices: Evidence from Canada and the United States," Journal of International Money and Finance, Elsevier, vol. 29(5), pages 876-896, September.
    8. Tsouma, Ekaterini, 2009. "Stock returns and economic activity in mature and emerging markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 668-685, May.
    9. Tomasz Wozniak, 2015. "Granger-causal analysis of GARCH models: a Bayesian approach," Department of Economics - Working Papers Series 1194, The University of Melbourne.
    10. Arne J. Nagengast & Robert Stehrer, 2016. "The Great Collapse in Value Added Trade," Review of International Economics, Wiley Blackwell, pages 392-421.
    11. repec:kap:iecepo:v:14:y:2017:i:4:d:10.1007_s10368-016-0355-1 is not listed on IDEAS
    12. Karoline Krätschel & Torsten Schmidt, 2012. "Long-run Trends or Short-run Fluctuations – What Establishes the Correlation between Oil and Food Prices?The Interplay of Standardized Tests and Incentives – An Econometric Analysis with Data from PIS," Ruhr Economic Papers 0357, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    13. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, pages 418-432.
    14. Belke, Ansgar & Beckmann, Joscha & Verheyen, Florian, 2013. "Interest rate pass-through in the EMU – New evidence from nonlinear cointegration techniques for fully harmonized data," Journal of International Money and Finance, Elsevier, pages 1-24.
    15. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, pages 207-230.
    16. Tae-Hwy Lee & Weiping Yang, 2014. "Money-Income Granger-Causality in Quantiles," Working Papers 201423, University of California at Riverside, Department of Economics, revised Sep 2012.
    17. Robert Killins, 2016. "The Influence of Monetary Policy on Equity and Volatility Indices in the U.S. and Canada," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(4), pages 132-145, April.
    18. Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu, 2016. "Oil price and exchange rate in India: Fresh evidence from continuous wavelet approach and asymmetric, multi-horizon Granger-causality tests," Applied Energy, Elsevier, pages 272-283.
    19. Konstantinos N. Konstantakis & Panayotis G. Michaelides, 2015. "Step-by-Step Causality Revisited: Theory and Evidence," Economics Bulletin, AccessEcon, vol. 35(2), pages 871-877.
    20. Angelini, Giovanni & Bacchiocchi, Emanuele & Caggiano, Giovanni & Fanelli, Luca, 2017. "Uncertainty across volatility regimes," Research Discussion Papers 35/2017, Bank of Finland.
    21. Giovanni Angelini & Emanuele Bacchiocchi & Giovanni Caggiano & Luca Fanelli, 2017. "Uncertainty Across Volatility Regimes," CESifo Working Paper Series 6799, CESifo Group Munich.
    22. Bosupeng, Mpho, 2014. "Sensitivity Of Stock Prices To Money Supply Dynamics," MPRA Paper 77924, University Library of Munich, Germany, revised 2014.
    23. repec:zbw:rwirep:0357 is not listed on IDEAS

    More about this item

    Keywords

    Monetary and financial indicators;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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