Modelling Volatility in Nigerian Stock Market: Evidence from Skewed Error Distributions

: The use of skewed error distributions in volatility modelling has not been much given research attention in Nigeria. This study models the volatility in Nigerian stock market using skewed error distribution with the objective of determining the combination of volatility model and the skewed error distribution that best capture the dynamics in the volatility of Nigerian stock market. Using data on daily All Share Index(ASI) for Nigeria between 02/10/2001 and 29/03/2018, the study estimate the parameters of GARCH(1,1), APARCH(1,1), GJR-GRACH(1,1), IGARCH(1,1) and EGARCH(1,1) using skewed normal, skewed Student-t and skewed generalized error distributions. The parameters of these volatility models at each of the error distribution were estimated using rugarch package in R software. Result reveals that among the competing error distributions, in most of the models, the skewed normal distribution was found to be the best error distribution both in terms of fitness. Result of the 50 days trading out of sample forecast also favours the skewed normal distribution. Based on the least RMSE, the best forecasting model was recommended as APARCH (1,1)-skewed normal distribution. Result shows evidence of volatility clustering and high persistence of volatility indicating high level of risk and uncertainties in the Nigerian stock market. This signals possibilities of excessive gain or losses by investors but nevertheless the use of APARCH (1, 1)-skewed normal distribution in volatility prediction is hoped to help reduce the chances of excessive losses by investors and traders in the stock market. There is a need for investors and traders in Nigerian stock market to exercise caution while trading and the Nigerian Stock Exchange must come out with policies that will make Nigerian stocks less volatile. estimated stock market volatility using asymmetric GARCH models. Asymmetric GARCH models used in the study include GJR-GARCH, APARCH, and EGARCH. The study was carried out in Tel Avis Stock Exchange (TASE) in Israel and three distributional form of error innovation were used namely Student – t distribution, normal distribution, and skewed-Student-t distribution. The study also quantified the day of the week effect and the leverage effect on volatility of Tel Avis Stock Exchange in Israel. The result revealed that asymmetric GARCH model with fat tailed densities improved overall estimation of measuring conditional varies. The study also found that the Student-t distribution is the most successfully distribution for studying the volatility behaviour of 2006 to 2009. This model was estimated based on Student-t distribution, normal distribution, Skew Generalized Error Distribution (SGED). Result revealed that DAX is not like normal distribution as the stock was seen to exhibit high kurtosis. After comparing the “Generalized Autoregressive Conditional Heteroscedasticity (GARCH)” model at each of the six distributions of error innovations, based on log likelihood, Akaike Information Criteria (AIC) and forecasting ability, the Skew Generalized Error Distribution (SGED) was found to outperform the other five innovation distributions.