Trading volume volatility in Nigeria’s banking sector

Abstract

This paper explores related research directions and establishes the need for modelling financial market’s characterizations in Banking Sector using generalized autoregressive conditional heteroskedastic ity (GARCH) models. In this paper, a Bilinear Generalized Autoregressive Conditional Heteroskedastic (BL-GARCH (1, 1)) model which is capable of capturing simultaneously the volatility clustering and leverage effect present in high frequency financial data is introduced. Daily stock prices of four banks, purposely
selected from the Nigeria Stock Market (January 4, 2007 – May 15, 2018) were collected and analyzed. The results obtained through empirical study established heavy tails, volatility clustering and persistence of stock returns within the sample period. The asymmetric effect of bad news on volatility is higher when contemporaneous trading volume is included, although market shocks, whether positive or negative, have similar effects on conditional volatility. Thus, we conclude that the inclusion of trading volume in the BL-GARCH (1, 1) model is a veritable variable for enhancing the understanding of volatility dynamics of the selected banks, and by extension other mega banks in Nigeria.