IDEAS home Printed from https://ideas.repec.org/a/ids/ijbfmi/v2y2016i2p79-94.html
   My bibliography  Save this article

Asymmetric relationship between stock market returns and macroeconomic variables

Author

Listed:
  • N. Chitra Devi
  • S. Chandramohan

Abstract

The aim of the study is to examine the relationship between stock market returns and key macroeconomic variables in the UK. The method of Ordinary Least Square has been applied to find out the nexus between stock market returns and macroeconomic variables in the UK. The study reveals that the application of ordinary least square has not been BLUE due to the existence of conditional heteroskedasticity which is confirmed by the ARCH-LM test. Therefore, symmetric and asymmetric GARCH models have been employed to find out the nexus between macroeconomic variables with the stock market returns. The performance of symmetric and asymmetric models are compared using the model selection criterion namely Akaike information criterion which suggests that the EGARCH model is the best model in the UK. The result of the EGARCH model reveals that the impact of news on stock market returns is asymmetric in the UK stock market.

Suggested Citation

  • N. Chitra Devi & S. Chandramohan, 2016. "Asymmetric relationship between stock market returns and macroeconomic variables," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(2), pages 79-94.
    • Handle: RePEc:ids:ijbfmi:v:2:y:2016:i:2:p:79-94
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=78147
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    References listed on IDEAS

    as
    1. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    2. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    3. Gulnur Muradog Lu & Kivilcim Metin & Reha Argac, 2001. "Is there a long run relationship between stock returns and monetary variables: evidence from an emerging market," Applied Financial Economics, Taylor & Francis Journals, vol. 11(6), pages 641-649.
    4. Vanita Tripathi & Ritika Seth, 2014. "Stock Market Performance and Macroeconomic Factors: The Study of Indian Equity Market," Global Business Review, International Management Institute, vol. 15(2), pages 291-316, June.
    5. Miron, Dumitru & Tudor, Cristiana, 2010. "Asymmetric Conditional Volatility Models: Empirical Estimation and Comparison of Forecasting Accuracy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), September.
    6. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    7. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    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. Nagaraj Naik & Biju R. Mohan, 2021. "Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market," Mathematics, MDPI, vol. 9(14), pages 1-18, July.
    2. Massimiliano Marzo & Paolo Zagaglia, 2010. "Volatility forecasting for crude oil futures," Applied Economics Letters, Taylor & Francis Journals, vol. 17(16), pages 1587-1599.
    3. Daniele Bianchi & Massimo Guidolin & Francesco Ravazzolo, 2017. "Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 110-129, January.
    4. Heitham Al-Hajieh & Hashem AlNemer & Timothy Rodgers & Jacek Niklewski, 2015. "Forecasting the Jordanian stock index: modelling asymmetric volatility and distribution effects within a GARCH framework," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 4(2), pages 9-26.
    5. Roberto Ferulano, 2009. "A Mixed Historical Formula to forecast volatility," Journal of Asset Management, Palgrave Macmillan, vol. 10(2), pages 124-136, June.
    6. DUȚĂ, Violeta, 2018. "Using The Symmetric Models Garch (1.1) And Garch-M (1.1) To Investigate Volatility And Persistence For The European And Us Financial Markets," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 22(1), pages 64-86.
    7. Krzysztof DRACHAL, 2017. "Volatility Clustering, Leverage Effects and Risk-Return Tradeoff in the Selected Stock Markets in the CEE Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-53, September.
    8. Huang, Yirong & Luo, Yi, 2024. "Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    9. Francesco Guidi, 2009. "Volatility and Long-Term Relations in Equity Markets: Empirical Evidence from Germany, Switzerland, and the UK," The IUP Journal of Financial Economics, IUP Publications, vol. 0(2), pages 7-39, June.
    10. Abdessamad Ouchen, 2022. "Is the ESG portfolio less turbulent than a market benchmark portfolio?," Risk Management, Palgrave Macmillan, vol. 24(1), pages 1-33, March.
    11. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    12. Tarak Nath Sahu & Krishna Dayal Pandey, 2020. "Money Supply and Equity Price Movements During the Liberalized Period in India," Global Business Review, International Management Institute, vol. 21(1), pages 108-123, February.
    13. King, Daniel & Botha, Ferdi, 2015. "Modelling stock return volatility dynamics in selected African markets," Economic Modelling, Elsevier, vol. 45(C), pages 50-73.
    14. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    15. Aminullah Assagaf & Etty Murwaningsari & Juniati Gunawan & Sekar Mayangsari, 2021. "The Effect of Macro Economic Variables on Stock Return of Companies That Listed in Stock Exchange: Empirical Evidence from Indonesia," International Journal of Business and Management, Canadian Center of Science and Education, vol. 14(8), pages 108-108, July.
    16. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    17. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    18. Kambouroudis, Dimos S. & McMillan, David G., 2015. "Is there an ideal in-sample length for forecasting volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 114-137.
    19. M. Marzo & P. Zagaglia, 2007. "Domestic political constraints to foreign aid effectiveness," Working Papers 599, Dipartimento Scienze Economiche, Universita' di Bologna.
    20. Sumit Kumar Maji & Arindam Laha & Debasish Sur, 2020. "Dynamic Nexuses between Macroeconomic Variables and Sectoral Stock Indices: Reflection from Indian Manufacturing Industry," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 45(3), pages 239-269, August.

    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:ids:ijbfmi:v:2:y:2016:i:2:p:79-94. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=156 .

    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.