Some Stylized Facts of Short-Term Stock Prices of Selected Nigerian Banks

Abstract

This paper examines presence of some stylized facts of short-term stock prices in the banking sector of the Nigerian Stock Market (NSM). Non-normality, lack of autocorrelation in the returns at first lag and significant positive autocorrelation in higher magnitude returns, widely studied in other markets, are investigated using daily closing stock prices of the four major Nigerian banks (Access, First, Guaranty Trust and United Bank for Africa (UBA)), from 2001 to 2013; encompassing periods of different financial scenarios. Jarque-Bera (JB), Doonik-Hansen, Kolmogrov-Smirnov and Ljung-Box (Q) test statistics are applied. Our findings reveal that the four banks stocks behave slightly different, but generally possess the stylized facts found in other markets. Observed is that, while the distributions of the returns for two of these banks (First and UBA) are approximately symmetric and leptokurtic; those of Access and Guaranty Trust banks are significantly non-symmetric and leptokurtic, thus non-normally distributed. Also established is that, while autocorrelation functions of daily returns are either negative or zero, those of both absolute returns and the squared returns are mostly positive. The autocorrelations of absolute returns are found to be predominantly positive and more persistent than those of the squared returns; indicating volatility clustering. Consequently, we conclude that the short-term stock prices of these banks behave like those of other markets. Some implications of the results for financial investment and stock market behaviour in the banking sector of NSM are discussed.

Stochastic Ito-Calculus and Numerical Approximations for Asset Price Forecasting in the Nigerian Stock Market

“…Follow-on work to this paper will approach the problem by benchmarking the Nigerian Stock Market (NSM) on the Johannesburg Stock Exchange (JSE), and using (for a simplified statement of the problem) comparative correlations of asset prices in the two markets to judge the degree of closeness of the markets and/or their sectors. This judgement will inform simulations of derivative prices that will fit the stylised facts of the NSM, [12]. These computational modelling under assumed scenarios requires an intimate understanding of closed-form numerical forecasting of asset prices which this paper explores.…”

Systematic Stock Market Characterisation and Development: Perspectives from Random Matrix Theory, Option Pricing, Genetics, and Global Economics

“…The SSMCD connections with these ideas consist in analysing share prices and returns data from different sectors of the NSM such as mentioned above, in light of stylised facts of the market, the six market issues where appropriate (efficiency, bubbles, anomalies, volatility, valuation, and predictability) and RMT correlation diagnostics within the sectors, across the sectors, and, for derivative pricing, implementing the heuristics benchmarked on the JSE. Research pioneered in the Statistics, Information Modelling and Financial Mathematics (SIMFIM) research group, Sheffield Hallam University, UK, and intensified in the Schools of Statistics and Information Modelling and Global Economics at the International Centre for Research and Enterprise Development (ICRED), www.oseluxworldhero3e.com, has characterised the banking sector of the NSM involving twenty-five major banks, [58]) and [6]. The market features examined include stylised facts [6], market efficiency, bubbles, anomalies and volatility [59].…”

“…Research pioneered in the Statistics, Information Modelling and Financial Mathematics (SIMFIM) research group, Sheffield Hallam University, UK, and intensified in the Schools of Statistics and Information Modelling and Global Economics at the International Centre for Research and Enterprise Development (ICRED), www.oseluxworldhero3e.com, has characterised the banking sector of the NSM involving twenty-five major banks, [58]) and [6]. The market features examined include stylised facts [6], market efficiency, bubbles, anomalies and volatility [59]. Also, SSMCD work was initiated in a related and currently on-going PhD research on "Macroeconomic Modelling of Oil Prices and Financial Variables Libya and Nigeria".…”

“…This is because SSMCD results triangulate different approaches to financial and economic analysis as explained around Table 1 in this paper. Following [2], additional research within the Statistics, Information Modelling and Financial Mathematics Research Group at Sheffield Hallam University, UK, was conducted on share prices and returns of Nigerian banks [6]. The results manifest evidence of bubbles in the banking sector, which were masked at the overall level in [2].…”

“…The remaining eigenvectors appear to be random with some deviations from the predicted value of 3/N = 0.04 possibly because of the existence of fat tails and high kurtosis of the return distributions. The latter are aspects of stylized facts of asset returns in the NSM which describe related market dynamics and therefore underpin SSMCD research and applications ([3],[6] and[7]). Realistic Implied Correlation Matrix Computations:Suppose we had the following weights and implied volatility (computed from option prices) for the under listed assets drawn from the NSM.P.…”