**2. Literature review**

an oil dependent economy due to fall in the global oil price. This is usually due to the investors' perception that they may suffer huge financial loss if their investments are not quickly moved. Therefore, the stock market is so sensitive and important that it serves as long term funds for investment, businesses, financial institutions, private and the public. It is such that investors are much more concerned about the

Apart from the theoretical and the empirical support, the stock market returns have been further verified to respond to the global oil price during the period of study in Nigeria (**Figure 1**). Therefore, since oil price volatility has been the major source of uncertainty in stock market returns especially in an oil-dependent economy like the sample country, it is then imperative to study their relationships. The unpredictability in the movement of oil price and its correlation with stock price returns have made it imperative for financial investors, practitioners, risk managers and policy makers to be interested in appropriate volatility model that best predicts minimum variance of the stock returns. Some previous studies in Nigeria have examined volatility using GARCH models. Salisu [6] examined comparative performance of both Brent oil and Western Texas Intermediate (WTI) oil across subsamples in Nigeria using GARCH models and found that bad news in the oil market increased oil price than good news. Najjar [7] applied ARCH, GARCH and EGARCH to Amman stock exchange in Jordan to study the return volatility of the market and found GARCH model to explain the extent of volatility clustering and leptokurtosis in the stock market. Uyaebo et al. [8] used non-linear GARCH models on the all share index of six selected stock market of Nigeria, Kenya, Germany, South Africa, China and United States for the period of February 2000 to February 2013. The study found volatility to be faster and persist in Nigeria and Kenya only. The study by [9] also investigated volatility of banks' equity returns on weekly basis for six commercial banks using GARCH models from January 2010 to June 2016. The study found EGARCH and CGARCH as the best volatility model in Nigeria. This present paper is different from the previous papers and contributed to the literature in two ways. First, we used different error distributions in the estimation of the standard GARCH and the non-linear GARCH models which previous studies have failed to take into consideration. Second, this study extends into the period of the COVID-19, the first quarter of the year 2020, which is another period of global shocks to

volatility of their returns in terms of gain and losses.

*Linear and Non-Linear Financial Econometrics - Theory and Practice*

both the oil market and the stock market.

*The movement of change in oil price and stock price over the study period.*

**Figure 1.**

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The literature review on the relationship between stock price and oil price volatility in this study is done along the type of generalized autoregressive conditional heteroscedasticity GARCH type adopted. Hammoudeh and Aleisa [10] studied the causal relationship between oil price and stock price and found causality emanating from the variables for Saudi Arabia. Also, Bashar [11] examined the effects of oil price on stock market of five GCC countries such as Bahrain, Kuwait, Oman, Saudi Arabia and Abu Dhabi with daily data from the period of 25th May, 2001 to 24th May, 2005. The study found a bidirectional respond between Saudi stock market and the oil price shocks in vector autoregression (VAR) analysis.

In another paper for GCC, [12] investigated the volatility and channel of shocks among US equity market, global oil market and the equity market of Saudi Arabia, Kuwait and Bahrain. Of all the three equity markets, only Saudi Arabia equity market had significant volatility spillover to the oil market with the multivariate (GARCH) with BEKK. Arouri et al. [13] applied a generalized VAR-GARCH approach to examine the volatility channel between oil and stock market of Europe and the US. After analyzing the optimal weights and hedge ratio for oil-stock portfolio, the study found different volatility spillover for the selected European and the US stock market with VAR-GARCH being the best asset-hedging model. Khalfao et al. [14] investigated the relationship between West Texas Intermediate (WTI) crude oil market and the stock market of G-7 countries using wavelet-based MGARCH method. The mean and the variance of the study showed significant volatility spillover between the G-7 stock market returns and the oil market. Bouri [15] also applied ARMAX-GARCH to model and predict stock market returns of investors of oil-exporting countries like Lebanon and Jordan. The selected MENA countries are Morocco and Tunisia. The study found volatility spillover from the oil market to only Jordan stock market. In another paper, [16] examined directional connectedness between oil market and equity by applying implied volatility indices for 11 stock markets for the period of 2008–2015. A one-way transmission was found from oil market to equity market. Khamis et al. [17] used causality and multivariate regression method with daily data from the year 2012 to 2015 to examine the response of Saudi Arabia stock market to oil price fluctuation at the sectoral level. The finding is that Saudi Arabia stock market showed different to oil market. In recent paper, [18] examined the connection between oil price and stock market for net oil-exporting and net oil-importing countries such as Russia, Canada, United States and Japan using cointegration analysis. The study found significant and positive connection only between Russian stock market and oil price for the study period of 2007–2016.
