Oil Price In Nigeria Research Articles

Modeling and Forecasting Crude Oil Prices in Nigeria Using ARIMA: A Time Series Analysis from 2013-2022

Study’s Excerpt/Novelty This study employs the Box-Jenkins ARIMA model to forecast crude oil prices in Nigeria from 2013 to 2022, identifying the ARIMA (1, 1, 1) model as the most suitable based on AIC, BIC, and HQIC criteria. The findings emphasize the model’s adequacy for short-term price forecasting but highlight its limitations, particularly its sensitivity to non-stationary data. The study recommends incorporating macroeconomic variables or hybrid models for more accurate long-term predictions, while urging Nigerian oil sector stakeholders to explore alternatives like Liquefied Natural Gas (LNG) to mitigate revenue losses from declining crude oil sales. Full Abstract This study utilizes Box-Jenkins ARIMA modeling to forecast crude oil prices in Nigeria from 2013 to 2022. The data, sourced from the CBN statistical bulletin, revealed non-stationarity, corrected by first differencing. The ARIMA (1, 1, 1) model was identified as the most suitable based on AIC, BIC, and HQIC criteria, providing significant information on short-term price forecasting and model adequacy. The study concludes that while ARIMA models are effective for short-term forecasting, their limitations, such as sensitivity to non-stationary data, suggest that future research should incorporate macroeconomic variables or hybrid models for improved accuracy. It was recommended that stakeholders in the Nigerian oil sector should explore other avenues in the sector, such as Liquefied Natural Gas(LNG), in order to absorb the decrease in revenue as a result of the decline in revenue from crude oil prices and sales.

Oil and Food Price Before and During COVID-19 Pandemic in Nigeria: A Non-Linear ARDL Approach

Abstract This paper analyzes the relationship between food and oil prices in Nigeria before and during the COVID-19 pandemic, using monthly data from January 2018 to December 2021. The ARDL and NARDL models are applied to estimate the symmetry and asymmetric relationship that exists in food price behavior. The NARDL confirms the presence of asymmetries, and the bound test affirms the co-integration and long-run relationship among the variables. In the long run, there is a significant positive relation between oil price increases and food prices, but the long-run impact of oil price reductions on food prices is not significant. In the short run, only increases in oil prices exert a significant influence on food prices, while decreases in oil prices do not. Furthermore, the COVID-19 period exerts a positive and significant impact on food prices, while COVID-19 cases do not influence food prices in Nigeria.

Computational Modelling of the Nigeria’s Daily Crude Oil Prices During the Russia – Ukraine War.

The study on the computational modelling of Nigeria’s crude oil prices during the Russia – Ukraine war was conducted to determine and select an appropriate model that best describes the functional behaviour of crude oil prices in Nigeria during the Russia – Ukraine war. The research data was the daily price of crude oil prices and the time under review collected from the Central Bank of Nigeria website, and spanned the period from February 24, 2022, to June 13, 2023. The total observations summed up to three hundred and eighteen days (318). Six models were estimated for the study namely; linear, quadratic, cubic, log-linear, linear-log, and log-log models. Nevertheless, the Log-linear Model, which had the values (S= 0.045; R2 = 67.6%; F = 660.29; p=.000), and the smallest accuracy measures (MAE = 0.030; MSE = 0.002; MAPE = 0.034), is adjudged as the best-fit model among the six built regression models. As a result, the Log-linear estimation has the most impact on the crude oil prices of Nigeria during the period of the Russia –Ukraine war under review. The study hereby recommends forecasting crude oil prices using the log-linear model.

Enhancing Nigerian Oil Price Forecasting: A Comprehensive Analysis of Model Averaging Techniques

Numerous fields of endeavour have benefited greatly from statistical forecasting, which has aided decision-making by planners and policy makers. In this study, Bayesian Model Averaging (BMA) and Dynamic Model Averaging (DMA) are employed to forecast oil prices in Nigeria. It aimed at predicting the oil prices in Nigeria. Essentially, there are lot of model uncertainties in empirical growth researches. The predictive performance value considering the Mean Squared Forecast Error (MSFE) for BMA and DMA were 920.23 & 540.40 respectively. The DMA predicted the model better than the BMA. High levels of model uncertainties were indeed accounted for, in conformity with the theoretical knowledge.

The asymmetry of shocks in crude oil price in Nigeria

While studies in Nigeria have focused on the consequences of oil price shocks on the economy, they implicitly assumed that oil price changes are symmetrical without specifically testing for asymmetry. This study concentrated on the shock asymmetries and crude oil price volatility in Nigeria. Used were time series data for the years 1981 to 2022 from the Statistical Bulletin of the Central Bank of Nigeria and the World Bank. The data were analyzed using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, and the findings showed that there is a general tendency for oil price shocks to be around 1.83 percent bigger in declining oil prices than in rising prices. The asymmetric shocks to the price of oil completely dominate positive leverage. In order to minimize the economy's reliance on crude oil exports as its main export (and source of revenue), measures for export promotion and diversification should be put in place.

Modelling exchange rate volatility in turbulent periods: The role of oil prices in Nigeria

The oil price has been increasingly identified as a key fundamental in the dynamics of exchange rates. As a result, we investigate how changes in oil prices affect the dynamics of exchange rates during crisis periods. We hypothesised that the potential of oil prices as an amplifier of exchange rate volatility during crises varies for economic and non-economic crises with divergent origins. Consequently, we classified the crisis sample into two sub-samples: the great recession caused by the 2007 global financial crisis (GFC) is defined as a crisis sample with an economic origin, while the great lockdown caused by the recent COVID-19 outbreaks defined our crisis period with a non-economic origin. We used the GARCH model and its many extensions and noted three findings that strengthened our contributions to the empirical analysis of exchange rate volatility. First, we show that the divergent origins of economic and non-economic crises matter in terms of the extent to which they heighten exchange rate volatility. Second, the persistence of exchange rate volatility during COVID-19 is exacerbated by changes in international oil prices. Finally, our finding of varying dynamics of persistent (transient) exchange rate volatility across different samples of turbulent periods provides investors with evidence-based insights not to generalise their portfolio selection strategy amidst economic crises of different origins.

Predicting Crude Oil Price in Nigeria with Machine Learning Models

Background: The rise in crude oil prices yields serious consequences for both oil-producing and non-oil-producing countries. The increase in global commodity prices contributes to the financial income and foreign exchange reserves of oil-exporting countries. However, countries such as Nigeria that sell crude oil and purchase refined fuel confront more complicated situations. To this end, there exists a need to obtain a robust prediction model for the crude oil price of Nigeria. Objective: This study is to determine the best model among the machine-learning time series models considered to predict crude oil prices in Nigeria. Methods: The alternative models were the auto-regressive integrated moving average model, Naive Bayes, Holtwinter trend model, exponential smoothing model, and neural network autoregressive (NNETAR) model. The prediction criteria adopted for model screening were the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Daily crude oil prices in dollars obtained from the Central Bank of Nigeria were used for analysis spanning from October 1, 2009 to March 22, 2022 with 2836 data points. Results: The NNETAR model showed the minimum RMSE, MAE, and MAPE for cross-validation sets considered. Conclusion: The NNETAR model was recommended for the prediction of crude oil prices in Nigeria.

Time-Gap effects of crude oil prices on the foreign exchange rates: Evidence from Nigeria

There is a pool of techniques and methods in addressing dynamics behaviors in higher frequency data, prominent among them is the ARCH/GARCH techniques. In this paper, the various types and assumptions of the ARCH/GARCH models were tried in examining the dynamism of exchange rate and international crude oil prices in Nigeria. And it was observed that the Nigerian foreign exchange rates behaviors did not conform with the assumptions of the ARCH/GARCH models, hence this paper adopted Lag Variables Autoregressive (LVAR) techniques originally developed by Agung and Heij multiplier to examine the dynamic response of the Nigerian foreign exchange rates to crude oil prices. The Heij coefficient was used to calculate the dynamic multipliers while the Engel & Granger two-step technique was used for cointegration analysis. The results revealed an insignificant dynamic long-term response of the exchange rate to crude oil prices within the periods under review. The coefficient of dynamism was insignificantly in most cases of the sub-periods. The paper equally revealed that the significance of the dynamic multipliers depends greatly on external information about both market indicators which are two-way interactions. Thus, the paper recommends periodic intervention in the foreign exchange market by the monetary authorities to stabilize the market against any shocks in the international crude oil market, since crude oil is the main source of foreign exchange in Nigeria.

Does the Expected Crude Oil Price Influence Inflation in Nigeria?

The study examined the influence of expected oil price on inflation rate with the view of understanding the behavior of inflation as a result of shocks on the expected crude oil prices in Nigeria. The study employed an Autoregressive Distributed lag (ARDL) and Bound testing cointegration approach in order to examine both the short-run and the long-run effects of the variables. The estimation result shows that expected oil price has a significant and positive effect on inflation both in the long-run and short-run in Nigeria. Surprisingly, in the long-run, interest rate has a positive and significant effect on inflation rate as against apriori expectations. It is therefore recommended that the Nigerian government diversify its resource base, and rely less on crude oil that is more susceptible to oil price shocks, thereby reducing the effects of oil price on inflation in Nigeria. Secondly, the monetary authorities may need to use an effective interest rate policy to control the inflation rate, which in turn will stimulate investment.

Time-Gap effects of crude oil prices on the foreign exchange rates

There is a pool of techniques and methods in addressing dynamics behaviors in higher frequency data, prominent among them is the ARCH/GARCH techniques. In this paper, the various types and assumptions of the ARCH/GARCH models were tried in examining the dynamism of exchange rate and international crude oil prices in Nigeria. And it was observed that the Nigerian foreign exchange rates behaviors did not conform with the assumptions of the ARCH/GARCH models, hence this paper adopted Lag Variables Autoregressive (LVAR) techniques originally developed by Agung and Heij multiplier to examine the dynamic response of the Nigerian foreign exchange rates to crude oil prices. The Heij coefficient was used to calculate the dynamic multipliers while the Engel & Granger two-step technique was used for cointegration analysis. The results revealed an insignificant dynamic long term response of exchange rate to crude oil prices within the periods under review. The coefficient of dynamism was insignificantly in most cases of the sub-periods. The paper equally revealed that the significance of the dynamic multipliers depends greatly on external information about both market indicators which are two-way interactions. Thus, the paper recommends periodic intervention in the foreign exchange market by the monetary authorities to stabilize the market against any shocks in the international crude oil market, since crude oil is the main source of foreign exchange in Nigeria.

Modelling the Nigeria Crude Oil Prices Using ARIMA, Pre-intervention and Post-intervention Model

The study applied Autoregressive Integrated Moving Average Intervention in modelling crude oil prices in Nigeria spanning the period from January 1986 to June 2017. The time plot of the series showed an abrupt increase in the series and this called for intervention modelling. The data was divided into three set (actual series, pre-intervention and post-intervention series). The Augmented Dickey Fuller (ADF) was used to test for unit root on each of the series and were all found to be non-stationary at levels, they (actual, pre and post- intervention series) were however non stationary at first difference. Eighteen models were estimated and the best model was the pre-intervention model that minimise the Akaike information criterion (AIC) (ARIMA (111)) with AIC of (4.4.578). The plot of the residual correlogram showed adequacy of the model. The model was adequate since there was no spike that cut the level of the correlogram and the histogram of the residual was normally distributed with probability values (0.0000).

Modeling Short Run Relationship between Naira-USD Exchange Rate and Crude Oil Price in Nigeria

The vector auto-regressive (VAR) model is one of the most successful, flexible, and easy to use models for the analysis of multivariate time series. It is a natural extension of the univariate auto-regressive model to a dynamic multivariate time series.The VAR model has proven to be especially useful for describing the dynamic behavior of economic and financial time series and for forecasting. It often provides superior forecasts to those from univariate time series models. The data used are monthlyobservations from January 2006 to October 2016 of Nigeria Crude Oil price and Naira to the dollar exchange rate. The VAR model was employed for modelling the data. The unit root test reveals that all the series are non-stationary at the level and stationary at first difference. The co-integration relations among the series indices were identified by applying Johansen’s cointegration test. The result of Johansen’s test indicates no existence of co-integration relation between the variables. The final result shows that a vector autoregressive (VAR) model of lag three with no co-integration equations best fits the data.

Symmetric volatility forecast models for crude oil price in Nigeria

Symmetric volatility forecast models for crude oil price in Nigeria

Finance and growth in oil‐dependent economies: does crude oil price matter? evidence from Nigeria

AbstractThis study examines three possible causal relationships between financial sector development, economic growth and crude oil price in Nigeria using the Autoregressive Distributed Lag (ARDL) approach to cointegration analysis over the period 1981 to 2011. First, the causal effect of financial sector development on economic growth is found to be negatively insignificant in the long‐run and negatively significant in the short‐run suggesting the weakness of financial intermediary sector in resource mobilisation and allocation in Nigeria. Crude oil price is found to be the key driver of economic growth in Nigeria. Second, the causal effect of economic growth on financial sector development is as well found to be negatively insignificant in the long‐run and negatively significant in the short‐run. Third, the causal effect of crude oil price on financial sector development is found to be positive and significant in the long‐run, suggesting that crude oil price is the underlying factor that determines the amount of economic activities passing through the Nigerian financial sector. The results highlight the need for a well‐articulated policy framework that will lessen the high dependence of the Nigerian economy on crude oil.

Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria

This paper explores the application of artificial neural network in volatility forecasting. A recurrent neural network has been integrated in to GARCH model to form the hybrid model called GARCH-Neural model. The emphasis of the research is to investigate the performance of the variants of Backpropagation algorithms in training the proposed GARCH-neural model. In the first place, EGARCH (3, 3) was identified in this paper most preferred model describing crude oil price volatility in Nigeria. Similarly, Levenberg-Marquardt (LM) training algorithms were found to be fastest in convergence and also provide most accurate predictions of the volatility when to other training techniques.

The Impact of Oil Price Volatility on the Real Exchange Rate in Nigeria: An Error Correction Model

In this paper, we empirically investigated the link between real exchange rates and real oil prices in Nigeria. We studied to find out whether the real exchange rate is positively related to the real oil prices as predicted by the models in previous research. We also investigated whether real oil prices can predict long-run movements in real exchange rates. The Error Correction Method (ECM) of analysis was used in the investigation.

Crude oil price dynamics and transmission mechanism of the macroeconomic indicators in Nigeria

The preoccupation of this study is to investigate the dynamic impacts of crude oil price on the economic growth of Nigeria. The technique of estimation used in this study is the structural vector autoregressive type. This method is applied to articulate the transmission mechanism of macroeconomic effects of domestic price level, economic output, money supply and volatile crude oil price in Nigeria. The study sample covers the period between 1985 and 2010. The data used are on a quarterly basis. The results of both the impulse response functions and the forecast error variance decompositions indicate that domestic shocks are responsible for a reasonable portion of crude oil price fluctuations. Although crude oil price volatility has significant positive impacts on economic output, however, money supply shocks are the main cause of gross domestic product fluctuations. This study concludes that crude oil price has very important impact on the Nigerian economy and the monetary policy is the channel through which this impact transmits.

Salt gradient solar ponds for the tropics: technical and economic appraisal for Nigeria

SYNOPSIS Agricultural, industrial, commercial and residential applications of solar ponds in the tropics have been studied and it was found that about 100W/m2 power flux would need to be withdrawn from the pond to prevent boiling and that maximum temperatures close to 200°C could, in theory, be attained with heat loads of 40W/m2 in a pond of 1-m insulating layer. Furthermore, the optimum collection temperature in the steady state solar pond in average Nigerian conditions is about 86°C. While small solar ponds for agricultural and industrial applications (area <500m2) were found to be economic when compared with electricity rates in Nigeria, the opposite was found in the case of large scale solar pond power stations (SPPS) of megawatt sizes. In both small and large scales, the solar pond was highly unattractive economically when compared with low natural gas and oil prices in Nigeria.