Ruhuna Journal of Management and Finance Volume 1 Number 1 - January 2014 ISSN 2235-9222 R M F J 73 Volatility of the Banking Sector Stock Returns in Nigeria K.O. Emenike and W.U. Ani K.O. Emenike * and W.U. Ani † *Department of Banking and Finance, Rhema University, Aba, Abia State, Nigeria. emenikekaluonwukwe@yahoo.com † Department of Banking and Finance, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria. Wilsonani2007@yahoo.com Abstract This paper examines the nature of volatility of stock returns in the Nigerian bank- ing sector using GARCH models. Individual bank indices and the All-share Index of the Nigerian Stock Exchange were evaluated for evidence of volatility persis- tence, volatility asymmetry and fat tails using data from 3 rd January 2006 to 31 st December 2012. Results obtained from GARCH models suggest that stock returns volatility of the Nigerian banking sector move in cluster and that volatility per- sistence is high for the sample period. The results also indicate that stock returns distribution of the banking sector is leptokurtic and that sign of the innovations have insignifcant infuence on the volatility of stock returns of the banks. Finally, the fndings of this study show that the degree of volatility persistence is higher for the All Share Index than for most of the banks. Keywords: banking sector; GARCH models; Nigeria; stock return volatility 1. Introduction Volatility of stock return is a measure of dispersion around the average return of a security or an index. Investigating behaviour of stock returns volatility gained momentum with the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model by Engle (1982) and its generalization by Bollerslev (1986). As a result, many variants of the GARCH model have evolved and understanding of volatility has improved steadily. The understanding of volatility of stock return is of crucial importance to stock market participants as variation of returns from expectation could mean huge losses or gain and hence greater uncertainty (Gujarati, 2003: 856). Again, a portfolio manager may want to sell a stock before it becomes too volatile or a market maker may want to set the bid-ask spread wider when the future is expected to be more volatile. Moreover, stock market regulators are interested in understanding volatility behaviour because high volatile stock market increases uncertainty, which reduces investors’ confdence in the market, and lead to high cost of capital. The behaviour of volatility has extensively been studied, surveyed and many stylized facts documented. One of the frst stylized facts of volatility of asset prices is volatility clustering. Mandelbrot (1963) and Fama (1965) both provide evidence to show that large changes in price of an asset are followed by large changes (of either sign) and small changes are often followed by small changes. This behaviour of volatility has been confrmed in both developed and emerging