Buba Audu | American University of Nigeria, Yola (original) (raw)

Papers by Buba Audu

Stock market volatility is of essential concern, particularly to two major stakeholders. While th... more Stock market volatility is of essential concern, particularly to two major stakeholders. While the practitioner looks through his own lenses with the bird’s-eye-view, he or she bothers himself or herself about the consequences of this behaviour on asset pricing and risk. Conversely, policy makers are burdened with the incidence of financial challenges and macroeconomic instability posed by the stock market phenomenon. Of optimum concern of these dual effects of stock market volatility, emanates predominantly from developing countries with infant stock markets, characterized by vulnerabilities. This is because, developing economies may parade market indices not explicitly possessed by their superior developed counterparts. Consequently, due to the disparities inherent in different context, the application of a single-most model in unravelling the effects of stock market volatility may be contentious or inaccurate. Therefore, the objective of this study is to develop a hybrid volatili...

The Journal of Finance and Data Science, 2017

Many researchers documented that if stock markets' returns series are significantly skewed, linea... more Many researchers documented that if stock markets' returns series are significantly skewed, linear-GARCH (1,1) grossly underestimates the forecast values of the returns. However, this study showed that the linear Maximal Overlap Discreet Wavelet Transform MODWT-GARCH (1,1) actually gives an accurate forecast value of the returns. The study used the daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014. The Maximal Overlap Discreet Wavelet Transform-GARCH (1,1) model and the Maximal Overlap Discreet Wavelet Transform-EGARCH (1,1) model are exhaustively compared. The results show that although both models fit the returns data well, the forecast produced by the Maximal Overlap Discreet Wavelet Transform-EGARCH (1,1) model actually underestimates the observed returns whereas the Maximal Overlap Discreet Wavelet Transform-GARCH (1,1) model generates an accurate forecast value of the observed returns.

The Journal of Finance and Data Science, 2016

The daily returns of four African countries' stock market indices for the period January 2, 2000,... more The daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014, were employed to compare the GARCH (1,1) model and a newly proposed Maximal Overlap Discreet Wavelet Transform (MODWT)-GARCH (1,1) model. The results showed that although both models fit the returns data well, the forecast produced by the GARCH (1,1) model underestimates the observed returns whereas the newly proposed MODWT-GARCH (1,1) model generates an accurate forecast value of the observed returns. The results generally showed that the newly proposed MODWT-GARCH (1,1) model best fits returns series for these African countries. Hence the proposed MODWT-GARCH should be applied on other context to further verify its validity.

Journal of Computer Science & Computational Mathematics, 2015

We used the monthly share prices of all Nigeria Share Price index from April, 2000 to January, 20... more We used the monthly share prices of all Nigeria Share Price index from April, 2000 to January, 2014 to identify and model the volatility of asset return in the Nigerian Stock Exchange. We compared several ARMA-GARCH models that best fits the series. The result of our study shows that the ARMA(1,1)-GARCH (1,1) model best describes the volatility of the return. The volatility of the returns was found to be quite persistent, i.e. current volatility can be explained by past volatility that tends to persist over time.

Stock market volatility is of essential concern, particularly to two major stakeholders. While th... more Stock market volatility is of essential concern, particularly to two major stakeholders. While the practitioner looks through his own lenses with the bird’s-eye-view, he or she bothers himself or herself about the consequences of this behaviour on asset pricing and risk. Conversely, policy makers are burdened with the incidence of financial challenges and macroeconomic instability posed by the stock market phenomenon. Of optimum concern of these dual effects of stock market volatility, emanates predominantly from developing countries with infant stock markets, characterized by vulnerabilities. This is because, developing economies may parade market indices not explicitly possessed by their superior developed counterparts. Consequently, due to the disparities inherent in different context, the application of a single-most model in unravelling the effects of stock market volatility may be contentious or inaccurate. Therefore, the objective of this study is to develop a hybrid volatili...

The Journal of Finance and Data Science, 2017

Many researchers documented that if stock markets' returns series are significantly skewed, linea... more Many researchers documented that if stock markets' returns series are significantly skewed, linear-GARCH (1,1) grossly underestimates the forecast values of the returns. However, this study showed that the linear Maximal Overlap Discreet Wavelet Transform MODWT-GARCH (1,1) actually gives an accurate forecast value of the returns. The study used the daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014. The Maximal Overlap Discreet Wavelet Transform-GARCH (1,1) model and the Maximal Overlap Discreet Wavelet Transform-EGARCH (1,1) model are exhaustively compared. The results show that although both models fit the returns data well, the forecast produced by the Maximal Overlap Discreet Wavelet Transform-EGARCH (1,1) model actually underestimates the observed returns whereas the Maximal Overlap Discreet Wavelet Transform-GARCH (1,1) model generates an accurate forecast value of the observed returns.

The Journal of Finance and Data Science, 2016

The daily returns of four African countries' stock market indices for the period January 2, 2000,... more The daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014, were employed to compare the GARCH (1,1) model and a newly proposed Maximal Overlap Discreet Wavelet Transform (MODWT)-GARCH (1,1) model. The results showed that although both models fit the returns data well, the forecast produced by the GARCH (1,1) model underestimates the observed returns whereas the newly proposed MODWT-GARCH (1,1) model generates an accurate forecast value of the observed returns. The results generally showed that the newly proposed MODWT-GARCH (1,1) model best fits returns series for these African countries. Hence the proposed MODWT-GARCH should be applied on other context to further verify its validity.

Journal of Computer Science & Computational Mathematics, 2015

We used the monthly share prices of all Nigeria Share Price index from April, 2000 to January, 20... more We used the monthly share prices of all Nigeria Share Price index from April, 2000 to January, 2014 to identify and model the volatility of asset return in the Nigerian Stock Exchange. We compared several ARMA-GARCH models that best fits the series. The result of our study shows that the ARMA(1,1)-GARCH (1,1) model best describes the volatility of the return. The volatility of the returns was found to be quite persistent, i.e. current volatility can be explained by past volatility that tends to persist over time.