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Asymmetric relationship between stock market returns and macroeconomic variables

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  • 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
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    References listed on IDEAS

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    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.
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