Modelling Nigeria stock market dynamics and interdependencies with Bayesian Vector Autoregression Model

Stock market in Nigeria is characterized by significant dynamic entity due to various factors, including economic and geographical instability, political events, regulatory changes, and global market influences. The study developed Vector Autoregressive (VAR) and Bayesian Vector Autoregressive (BVAR) models which allowed a thorough examination of the price dynamics among stock variables and offered insightful information about their interactions and significant influence on the stock price fluctuations in Nigeria. The study employed daily opening and closing prices for Dangote cement, Nestle, UBA, GT Bank and Zenith Bank obtained from the Nigerian Stock Exchange (NGX) at Lagos Island, Lagos State Nigeria. To ensure an extensive comprehension of price index dynamics and interdependence over time, collection of data covers the period of October 2010 to June 2024. The findings highlight the preeminence of the BVAR model as a more reliable and efficient tool for forecasting and analysis, as it accurately captures the complexities and dynamism of the relationship among the variables under study. The impulse response function revealed how changes in Nigerian price index had immediate effects on itself and others. This study provides significant leverage for financial analysts and stock market specialists in forecasting stock prices using the sectoral index's contribution and the relationships between other prices.