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An empirical study on the dynamic relationship between crude oil prices and Nigera stock market

Author

Listed:
  • Rabia Najaf

    (Department of Accounting & Finance, University of Lahore, Islamabad Campus)

  • Khakan Najaf

    (Department of Accounting & Finance, University of Lahore, Islamabad Campus)

Abstract

In this paper, we have examined the crude oil price on the performance of Nigerian stock exchange and exchange rate act as the plausible countercyclical tool .we have applied the different models and collected the results that crude oil prices have direct impact on the stock exchange of Nigeria. The Nigeria stock exchange is regulated by the Securities and Exchange Commission .Nigeria stock exchange has the automated trading system. The basic facility of Nigeria trading system is (ATS),it is helpful to remote trading system .Consequently, most of the investors do trade with the method of ATS .This study is also proving that Nigeria stock exchange has influenced on the performance of the economy. Impact of oil crisis on the Nigeria stock exchange.) Impact of crude oil crisis on the development of country. Effect of exchange rate policy on the performance of Nigeria stock exchange.

Suggested Citation

  • Rabia Najaf & Khakan Najaf, 2016. "An empirical study on the dynamic relationship between crude oil prices and Nigera stock market," International Journal of Academic Research in Management and Business, International Journal of Academic Research in Management and Business, vol. 1(2), pages 63-76, September.
    • Handle: RePEc:iap:ijarmb:v:1:y:2016:i:2:p:63-76
    as

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    References listed on IDEAS

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    1. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    2. N/A, 2001. "The World Economy," National Institute Economic Review, National Institute of Economic and Social Research, vol. 175(1), pages 29-58, January.
    3. Arouri, Mohamed El Hedi & Lahiani, Amine & Nguyen, Duc Khuong, 2011. "Return and volatility transmission between world oil prices and stock markets of the GCC countries," Economic Modelling, Elsevier, vol. 28(4), pages 1815-1825, July.
    4. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    5. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    6. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    7. N/A, 2001. "The World Economy," National Institute Economic Review, National Institute of Economic and Social Research, vol. 176(1), pages 35-60, April.
    8. Dhaoui, Abderrazak & Khraief, Naceur, 2014. "Empirical linkage between oil price and stock market returns and volatility: Evidence from international developed markets," Economics Discussion Papers 2014-12, Kiel Institute for the World Economy (IfW Kiel).
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