IDEAS home Printed from https://ideas.repec.org/a/wsi/rpbfmp/v09y2006i01ns0219091506000628.html
   My bibliography  Save this article

Who Leads the Australian Interest Rates in the Short and Long Run? An Application of Long Run Structural Modelling

Author

Listed:
  • A. Mansur M. Masih

    (Department of Finance & Economics, King Fahd University of Petroleum & Minerals, KFUPM P.O. Box # 1764, Dhahran 31261, Saudi Arabia)

  • Trent Winduss

    (LinQ Capital Limited, Perth, Western Australia)

Abstract

The focus of this paper is to test the cointegrating and Granger-causal relationships between Australian short-run interest rate securities and those of the UK, US, Japan, Hong Kong, Singapore and New Zealand. A relatively new methodology known as Long Run Structural Model (LRSM) (Pesaran and Shin, 2002) followed by vector error-correction model, generalized variance decompositions, generalized impulse response, and persistence profile have been used. The findings tend to suggest that Australia's short-term interest rates are cointegrated with those of its major trading partners. The results of this paper indicate that the ability of Australian policy makers to target and manipulate domestic interest rates may be limited and that they should look to the policy decisions of the US and Japan in particular when setting domestic policy.

Suggested Citation

  • A. Mansur M. Masih & Trent Winduss, 2006. "Who Leads the Australian Interest Rates in the Short and Long Run? An Application of Long Run Structural Modelling," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-24.
    • Handle: RePEc:wsi:rpbfmp:v:09:y:2006:i:01:n:s0219091506000628
    DOI: 10.1142/S0219091506000628
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219091506000628
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219091506000628?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shawtari, Fekri Ali & Masih, Mansur, 2017. "Granger-causal relationship between macroeconomic variables and stock prices: evidence from South Africa," MPRA Paper 99848, University Library of Munich, Germany.
    2. Rosle, Alia Nadira & Masih, Mansur, 2018. "Can the islamic banks’ credit risk be explained by macroeconomic shocks? evidence from Malaysia," MPRA Paper 107059, University Library of Munich, Germany.
    3. Kim, Suk-Joong & Nguyen, Do Quoc Tho, 2008. "The reaction of the Australian financial markets to the interest rate news from the Reserve Bank of Australia and the U.S. Fed," Research in International Business and Finance, Elsevier, vol. 22(3), pages 378-395, September.
    4. Sapian, Safeza & Masih, Mansur, 2018. "Do macroeconomic factors affect the credit risk of islamic banks? evidence from Malaysia," MPRA Paper 100719, University Library of Munich, Germany.
    5. Abbas, Amir & Masih, Mansur, 2017. "Islamic stock index, conventional stock index and macroeconomic variables," MPRA Paper 104806, University Library of Munich, Germany.
    6. Roland Füss & Denis Schweizer, 2012. "Short and long-term interactions between venture capital returns and the macroeconomy: evidence for the United States," Review of Quantitative Finance and Accounting, Springer, vol. 38(3), pages 391-410, April.
    7. Konstantinos Vergos & Benjamin Wanger, 2019. "Evaluating interdependencies in African markets A VECM approach," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 65-85.

    More about this item

    Keywords

    Australian economic integration; cointegration; Granger causality; long run structural modelling;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance

    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:wsi:rpbfmp:v:09:y:2006:i:01:n:s0219091506000628. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/rpbfmp/rpbfmp.shtml .

    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.