IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/52537.html
   My bibliography  Save this paper

Does Monetary Policy cause Randomness or Chaos? A Case Study from the European Central Bank

Author

Listed:
  • Sanderson, Rohnn

Abstract

Using the HICP (Harmonized Index of Consumer Prices) the author tests the series for the makeup of its dynamic components both before and after the start of stage three of the European Central Bank’s (ECB) monetary policy directive. While it appears ECB is meeting its stated objective, it is perhaps more important to address the composition of the lag and volatility of monetary policy to see how a policy change alters the fundamental dynamic structure of an economic system. The HICP data provides a good natural experiment for assessing structural change. This is important because while a policy may achieve its goal(s), in doing so it may alter the fundamental nature of how that system behaves, potentially causing the system to be more volatile or more sensitive to exogenous shocks in the future. Changes to the fundamental nature of a dynamic system can mean that future policies, that are similar to the present policies, could have very different impacts on that very same system in terms of both long run and short run effects. The paper finds that while the ECB may be meeting its stated objectives, it may be potentially increasing the degree and severity of future short run deflationary/inflationary cycles from similar policies in the future due to the type of random and deterministic components in the system. More data and further study is needed to determine the long-term affects of monetary policy in economic systems as many economic cycles are indeed very long.

Suggested Citation

  • Sanderson, Rohnn, 2013. "Does Monetary Policy cause Randomness or Chaos? A Case Study from the European Central Bank," MPRA Paper 52537, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:52537
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/52537/1/BBS_en_2013_04_Sanderson.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baumol, William J & Benhabib, Jess, 1989. "Chaos: Significance, Mechanism, and Economic Applications," Journal of Economic Perspectives, American Economic Association, vol. 3(1), pages 77-105, Winter.
    2. Fama, Eugene F & French, Kenneth R, 1988. "Permanent and Temporary Components of Stock Prices," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 246-273, April.
    3. Hsieh, David A, 1991. "Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    4. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    5. Milton Friedman, 1961. "The Lag in Effect of Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 69(5), pages 447-447.
    6. Howitt, Peter & Clower, Robert, 2000. "The emergence of economic organization," Journal of Economic Behavior & Organization, Elsevier, vol. 41(1), pages 55-84, January.
    7. Rohnn Sanderson, 2011. "Compartmentalising Gold Prices," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 4(2), pages 99-124, August.
    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. Mouck, T., 1998. "Capital markets research and real world complexity: The emerging challenge of chaos theory," Accounting, Organizations and Society, Elsevier, vol. 23(2), pages 189-203, February.
    2. Charilaos Mertzanis, 2013. "Risk Management Challenges after the Financial Crisis," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 42(3), pages 285-320, November.
    3. Barnett, William A. & Serletis, Apostolos & Serletis, Demitre, 2015. "Nonlinear And Complex Dynamics In Economics," Macroeconomic Dynamics, Cambridge University Press, vol. 19(8), pages 1749-1779, December.
    4. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
    5. Anning Wei & Raymond M. Leuthold, 1998. "Long Agricultural Futures Prices: ARCH, Long Memory, or Chaos Processes?," Finance 9805001, University Library of Munich, Germany.
    6. Guglielmo Maria Caporale & Luis A. Gil‐Alana & James C. Orlando, 2016. "Linkages Between the US and European Stock Markets: A Fractional Cointegration Approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(2), pages 143-153, April.
    7. Bwo-Nung Huang & Chin Yang, 1995. "The fractal structure in multinational stock returns," Applied Economics Letters, Taylor & Francis Journals, vol. 2(3), pages 67-71.
    8. Cornelis A. Los, 2004. "Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data," Finance 0409033, University Library of Munich, Germany.
    9. Gerlich, Nikolas & Rostek, Stefan, 2015. "Estimating serial correlation and self-similarity in financial time series—A diversification approach with applications to high frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 84-98.
    10. Doyle, John R. & Chen, Catherine Huirong, 2012. "A multidimensional classification of market anomalies: Evidence from 76 price indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1237-1257.
    11. Kim, Jae H. & Shamsuddin, Abul, 2008. "Are Asian stock markets efficient? Evidence from new multiple variance ratio tests," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 518-532, June.
    12. David Chappel & Joanne Padmore & Julia Pidgeon, 1998. "A note on ERM membership and the efficiency of the London Stock Exchange," Applied Economics Letters, Taylor & Francis Journals, vol. 5(1), pages 19-23.
    13. Nelson C. Mark & Donggyu Sul, 2004. "The Use of Predictive Regressions at Alternative Horizons in Finance and Economics," Finance 0409032, University Library of Munich, Germany.
    14. Mauro Napoletano, 2018. "A Short Walk on the Wild Side: Agent-Based Models and their Implications for Macroeconomic Analysis," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(3), pages 257-281.
    15. Guglielmo Maria Caporale & Luis Gil-Alana, 2011. "The weekly structure of US stock prices," Applied Financial Economics, Taylor & Francis Journals, vol. 21(23), pages 1757-1764.
    16. B. Jirasakuldech & Riza Emekter & Unro Lee, 2008. "Business conditions and nonrandom walk behaviour of US stocks and bonds returns," Applied Financial Economics, Taylor & Francis Journals, vol. 18(8), pages 659-672.
    17. Goddard, John & Onali, Enrico, 2012. "Self-affinity in financial asset returns," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 1-11.
    18. Jonathan Manton & Anton Muscatelli & Vikram Krishnamurthy & Stan Hurn, "undated". "Modelling Stock Market Excess Returns by Markov Modulated Gaussian Noise," Working Papers 9806, Business School - Economics, University of Glasgow.
    19. Claudeci Da Silva & Hugo Agudelo Murillo & Joaquim Miguel Couto, 2014. "Early Warning Systems: Análise De Ummodelo Probit De Contágio De Crise Dos Estados Unidos Para O Brasil(2000-2010)," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 110, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    20. Doyle, John R. & Chen, Catherine H., 2013. "Patterns in stock market movements tested as random number generators," European Journal of Operational Research, Elsevier, vol. 227(1), pages 122-132.

    More about this item

    Keywords

    dynamic systems; Hurst exponent; chaos; long-term memory; monetary policy;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:52537. 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.