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Johansson, A. (2010). Stock and Bond Relationships In Asia. Cerc Working Paper 14 April 2010

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Dynamic Linkages of Stock Market Sectors: Evidence from the Kenyan Stock Market

1School of Business, Kenyatta University, Nairobi, Kenya

2School of Economics, Kenyatta University, Nairobi, Kenya

3School of Business and Public Management, KCA University, Nairobi, Kenya


Journal of Finance and Economics. 2020, Vol. 8 No. 1, 33-39
DOI: 10.12691/jfe-8-1-5
Copyright © 2020 Science and Education Publishing

Cite this paper:
Eddie Simiyu, Julius Korir, Gabriel Laiboni. Dynamic Linkages of Stock Market Sectors: Evidence from the Kenyan Stock Market. Journal of Finance and Economics. 2020; 8(1):33-39. doi: 10.12691/jfe-8-1-5.

Correspondence to: Gabriel  Laiboni, School of Business and Public Management, KCA University, Nairobi, Kenya. Email: laiboni@kca.ac.ke

Abstract

This paper undertakes an empirical investigation into the dynamic linkages of the Nairobi Securities Exchange’s sectors. Using weekly indices for the 9th June 2008 to 14th February 2019 period, it examines the dynamic linkages of the Investment, Manufacturing and Allied, and Banking sectors. The study finds out the presence of one cointegrating relationship in the long run. A Vector Error Correction Model is fitted to estimate the temporal relationships of the three indexes. Results of Granger Causality testing, which are based on the VECM output, indicate the presence of bidirectional granger causality between the Manufacturing and Allied & Banking sectors as well as the Banking & Investment sectors. However, there is no granger causality between the Investment & Manufacturing and Allied sectors. Impulse response analysis show that shocks to the Manufacturing and Allied Sector are least significant in terms of their ability to invoke responses from other sectors, while shocks to the Banking sector are most influential as far are the tendency of elevating the volatility of other sectors’ indexes is concerned. Forecast Error Variance Decomposition (FEVD) analysis further indicate that shocks to the banking sector are most influential in terms of their explanatory power of other sectors’ forecast variances. The study therefore concludes that the banking sector has the highest tendency to influence other sectors’ volatility, while the Manufacturing and Allied Sector is least significant in terms of its ability to influence other sectors’ volatility. Therefore, it is recommended that that stocks that are listed under the Manufacturing and Allied Sector should be considered for diversification purposes due to the low scale of linkages that this sector has with other sectors. But, better still; investors should seek inter-market diversification opportunities in order to tap fully into the benefits of portfolio diversification.

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