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Measuring the financial risk exposure of the Nigerian commercial banks share prices in the presence of innovation densities

Author Affiliations

  • 1Department of Mathematics/Statistics/Computer Science, Federal University of Agriculture, Makurdi, Benue State, Nigeria

Res. J. Mathematical & Statistical Sci., Volume 7, Issue (2), Pages 1-12, May,12 (2019)

Abstract

Volatility modelling as a tool for measuring financial risk exposure as well as uncertainty is an important tool for many financial and economic applications. Banks and other financial institutions often make volatility assessment as a mean of monitoring their level of financial risk exposure. This study measures the level of financial risk exposure of some selected Nigerian commercial banks stock prices using symmetric and asymmetric GARCH models with non-Gaussian errors. The study utilised daily closing share prices of thirteen selected commercial banks listed on the Nigerian stock exchange (NSE) from 17/02/200 to 24/06/2016. The study employed Ng-Perron modified unit root test, Engle's Lagrange Multiplier test for ARCH effect, GARCH (1,1), GARCH (1,1)-M, EGARCH (1,1) and TARCH (1,1) models with student's-t and Generalized Error Distribution (GED) as methods of analysis. Result showed that the banking stock returns were stationary with non-normality behaviour and the residuals of returns were found to be heteroskedastic. All the estimated GARCH models were found to be stable, stationary and mean reverting. The volatility shocks were quite persistence and the news impact on the conditional variance was asymmetric across the banking stock returns. The study found mixed positive and negative tradeoff relationship between risk and the expected return across the banking stocks. Leverage effects were found to exist in seven commercial banks while there were no leverage effects in six commercial banks. The levels of financial risk exposure of the thirteen selected Nigerian commercial banks were found to be minimal and tolerable as each banking stock return mean reverts to its long-run average level. The study recommended some policy implications for both investors and policy makers.

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