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0ORIGINAL PAPER
DOI: 10.26794/2308-944X-2023-11-4-61-71 UDC 338.27(045) JEL E37, F51, Q34
Analysis of Crude Oil Market Volatility and Macroeconomic Conditions: Empirical Evidence from Nigeria
N. Musa
Kogi State University, Anyigba, Nigeria
ABSTRACT
This study aims to investigate the relationship between the volatility of the crude oil market and the macroeconomic conditions in Nigeria. The author used the methods of the auto-regressive distributed lag (ARDL) model in conjunction with the generalized autoregressive conditional heteroscedasticity (GARCH) to determine the extent of volatility using a monthly dataset from January 2012 to December 2022. The author regressed the crude oil price volatility index on Organization of the Petroleum Exporting Countries (OPEC) production quotas, conflicts, GDP growth rate, exchange rate and inflation. The results indicate that oil price volatility relates negatively to GDP, implying that the volatility of crude oil prices dampens growth in Nigeria. The paper concludes that rising oil prices heighten inflation, depreciate the exchange rate and depress growth in Nigeria. To hedge against oil price volatility, the paper recommends that the Nigerian government adopt policy measures that would increase energy efficiency and reduce the country's dependency on oil exports through diversification in other related productive sectors such as agriculture and manufacturing.
Keywords: oil price; volatility; geopolitical risk index; conflict; GDP; inflation; exchange rate; macroeconomics; Nigeria
For citation: Musa N. Analysis of crude oil market volatility and macroeconomic conditions: Empirical evidence from Nigeria. Review of Business and Economics Studies. 2023;11(4):61-71. DOI: 10.26794/2308-944X-2023-11-4-61-71
ОРИГИНАЛЬНАЯ СТАТЬЯ
Анализ волатильности рынка сырой нефти и макроэкономических показателей: эмпирические данные из Нигерии
Н. Муса
Университет штата Коги, Аньигба, Нигерия
АННОТАЦИЯ
Целью данного исследования является изучение взаимосвязи между волатильностью рынка сырой нефти и макроэкономическими показателями в Нигерии. Автор использовал методы авторегрессионного распределенного лага (ARDL) в сочетании с обобщенной авторегрессионной условной гетероскедастич-ностью (GARCH) для определения степени волатильности рынка на основе ежемесячных данных с января 2012 по декабрь 2022 г. Автор проводит регрессионный анализ индекса волатильности цен на сырую нефть и квот добычи Организации стран - экспортеров нефти (ОПЕК), конфликтов, темпов роста ВВП, обменного курса и инфляции. Результаты показывают, что волатильность цен на нефть отрицательно связана с ВВП. Это означает, что волатильность цен на сырую нефть снижает темпы роста в Нигерии. В статье делается вывод, что рост цен на нефть усиливает инфляцию, обесценивает обменный курс и снижает темпы роста в Нигерии. Чтобы избежать зависимости от волатильности цен на нефть, автор статьи рекомендует правительству Нигерии принять политические меры, которые позволят повысить энергоэффективность и снизить зависимость страны от экспорта нефти путем диверсификации в смежные производственные сектора, такие как сельское хозяйство и обрабатывающая промышленность.
© Musa N., 2023
Ключевые слова: цена на нефть; волатильность; индекс геополитического риска; конфликт; ВВП; инфляция; обменный курс; макроэкономика; Нигерия
Для цитирования: Муса Н. Анализ волатильности рынка сырой нефти и макроэкономических показателей: эмпирические данные из Нигерии. Review of Business and Economics Studies. 2023;11(4):61-71. DOI: 10.26794/2308-944X-2023-11-4-61-71
Introduction
Crude oil is one of the dominant mineral resources that Nigeria is endowed with. This makes the country one of the largest oil exporters in Africa [1]. In Nigeria, oil accounts for over 95% of export earnings, 25% of gross domestic product (GDP), and approximately 90% of government revenues.
Over the past few years, the global economy, including Nigeria, has experienced significant fluctuations in crude oil prices. The dynamics of world oil prices, in addition to demand and supply imbalances, is determined by several factors, including the actions of the Organization of Petroleum Exporting Countries (OPEC) to limit crude oil production (supply reduction), and heightened geopolitical events such as wars, terrorism and other political tensions such as the Iraq war, Gulf war, the Arab oil embargo, the global financial crisis of 2008-2009 and the ongoing Russian-Ukrainian conflict [2].
Nigeria has also experienced different geopolitical tensions, such as the Niger-Delta militancy and the Boko Haram insurgency. The activities of this group of terrorists led to massive destruction of property, pipe vandalism, crude oil theft, kidnapping and supply chain disruptions in Nigeria. It is pertinent to recall that in January 2023, after the removal of petroleum subsidies by the Nigerian Federal Government, Bonny Light oil prices swiftly rose to $110 per barrel, and diesel and gas prices also increased to N800 per litre (N stands for Nigerian currency, Naira; N800 approximately equals $1). Similarly, the price of Prime Motor Spirit (PMS) has also gone up from N195 to N617 per litre, with ripple effects on inflation pressures in the economy. Higher energy costs have the potential to push up commodity prices, production and transportation costs [3].
Fig. 1 shows the historical trend of oil prices. As shown above, crude oil prices were
$19.64, $21.54, $20.54 and $18.43 in 1989, 1991, 1993 and 1995, respectively. In 1999, crude oil prices averaged $19.35 per barrel. In 2005, the price increased to US$56.6 per barrel, and in 2007, it declined to US$55.8 per barrel. As reflected in the figure, the highest crude oil price was recorded in 2008, when it rose to US$145.29 per barrel, as against US$55.8 per barrel in the previous years. In 2009, the world entered a recession popularly known as the global financial crisis, which led to a sharp drop in oil price to US$53.4 per barrel. In 2010, the price rose to US$79.48, $94.88 in 2011, US$112 in 2014 and slumped to US$38.5 in 2015. In 2016, it increased to US$43.29 and in 2017, the price rose to US$50.8 and then to US$65.23 in 2018. However, between 2019 and 2020, crude oil prices dropped to US$56.99 and US$39.65, respectively, due to the COVID-19 pandemic. In 2021, the Brent crude oil price increased to US$70.86 per barrel, up from US$39.65 in previous years.
This was probably due to the post-pandemic recovery of the economies of China and other Asian Pacific region countries, leading to an increase in the demand for oil. On February 24, 2022, with the beginning of the Russian special military operation in Ukraine, Brent oil swiftly jumped to US$103.08 in February 2022 and rose to US$125.53 per barrel in May 2022. By December of the same year, the Brent price traded at US$103.93 per barrel. The short-term, sudden increase in oil prices in the spring of 2022, immediately after the start of the conflict, was purely speculative. In January 2023, due to the presence of a discount on Russian oil, the price dropped to US$83.42 and dwindled further to US$74.51 as of June 2023.
Although several studies on oil price volatility exist, such as [4-6]. None of these studies make concerted efforts to explore the correlation between the volatility of the crude oil market and macroeconomic conditions in Ni-
Fig. 1. Trends of Brent crude oil prices, 1989-2022 (US$;
Source: U. S. Energy Information Administration (EIA). URL: https://fred.stlouisfed.org/series/DCOILBRENTEU (accessed on 17.08.2023).
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geria. The majority of previous studies focused more on the nexus between oil price volatility and growth; more importantly, volatility models were not appropriately applied. Therefore, there is a need for an empirical examination of the nexus between the volatility of oil prices and the Nigerian macro-economy using appropriate models. This is the research gap addressed in the current paper.
The contributions of this paper to the existing body of knowledge are twofold. First, this research provides an empirical study on a topical issue that has gained little attention in the literature. Second, the findings would help us understand the dynamics of the world oil market and assist policymakers in adopting appropriate policy measures to counteract price shocks and alleviate long-term impacts on the economy.
This paper demonstrates novelty by adopting the GARCH model to test the extent of volatility in the oil market and by computing the geopolitical risk index for Nigeria, which was not covered by the existing geopolitical risk (GPR) index [7]. The practical significance of this study is that the findings would help us to understand the dynamics of world oil prices and the need to reduce the country's depend-
ency on oil exports through diversification of its export base to hedge against volatility.
The objective of this study is to investigate the relationship between the volatility of the crude oil market and the macroeconomic conditions in Nigeria. To achieve this goal, the study uses the GARCH model and the ARDL approach using a monthly dataset from January 2012 to December 2022.
Literature review
A study [8] found that wars and geopolitical tensions affect crude oil and stock market prices using time-varying parameter vector autoregressions (TVP/VAR) analysis. A study [9] also explored the effect of oil price volatility using structural VAR from 1991 to 2020. They found that oil price shocks lead to inflation in India. A study [10] investigated the effect of oil price shocks on oil-importing countries. They discovered that geopolitical risk increases oil prices, and oil-dependent countries are more sensitive to geopolitical risk. This finding is consistent with conclusions in other studies [11]. Similarly, a study [12] examined how geopolitical risk affects foreign direct investment outflows in China. Their findings show that geopolitical risk
is detrimental to foreign direct investment performance in China. An empirical study [13] on the impact of oil price shocks on Azerbaijan's economy using VAR showed a strong significant influence of crude oil price shocks on GDP. A study [14] on the impact of oil price changes in Malaysia from 1975 to 2015 used non-linear autoregressive distributed lags (NLARDL). Findings indicated that changes in crude oil prices contributed to GDP growth. The outcome in Malaysia is consistent with the findings by other scholars [9, 13].
Similar research on oil price shocks was also carried out in Nigeria. Ogungbenla [1] used data from 1980 to 2019 and employed the VAR regression technique. He found that volatile oil prices had a negative impact on real GDP using variables GDP, oil price (OILP), inflation (INF), and exchange rate (EXR). Using data from 2000 to 2018, study [4] found a similar investigation by applying the VAR regression technique. It was discovered that oil price shocks inversely affected GDP.
Maud and Evangelos [15] conducted research on oil price volatility from 1990 to 2021. The data were analyzed using the ARDL model. The results confirmed the existence of a negative association between oil prices and GDP growth. Their findings find support in previous studies [9]. In the same analysis, a study [4] used data from 1990 to 2012 to investigate oil price volatility. The data were analyzed using the ARDL model. The outcome of this research is consistent with [13]. A similar study was conducted [12] using structural vector auto-regression (SVAR) on a dataset from 1970 to 2010. The results confirmed that the volatility of oil prices negatively affected growth. Their findings also support those of [16-19].
Materials and Methods
The dataset used in this study contains monthly data and spans from January 2012 to December 2022. The period was selected based on the fact that Nigeria started experiencing major conflicts and terrorist attacks in oil-producing areas, which affected oil production levels. Data on GDP growth rate, inflation and exchange rate are obtained from the Central Bank of Nigeria Statistical Bulletin online da-
tabase.1 Data on Brent oil prices are from the U. S. Energy Information Administration (EIA), while data on OPEC oil production quotas are obtained from OPEC Annual Statistical Bulletin.2
We use the GPR index as an indicator of conflict. It is pertinent to note that the existing global GPR index by Caldara and La-coviello [7] did not cover Africa and Nigeria in particular.3 It only focused on developed and emerging economies without considering Africa. Our objective is to construct the GPR index for Nigeria, following the approach of Caldara and Lacoviello [7]. We calculate the monthly GPR index from January 2012 to December 2022 by counting the number of articles related to geopolitical events. Similar to [7, 20], we extracted relevant news articles from ten notable newspapers mentioning geopolitical events. These newspapers include Vanguard, This Day, The Punch, the Guardian, Independent Nigeria, Business Day, Daily Trust, Daily Champion, Nigerian Tribune, and P. M. News. We use relevant keywords (e.g., terrorism, terror, insecurity, bandits, Boko Haram, insurgent) relating to geopolitical events similar to the method of Caldara and Lacoviello [7], but with particular reference to Nigerian settings.
The generalized autoregressive conditional heteroscedasticity (GARCH (1, 1)) method was used to forecast crude oil price volatility. This approach is justified because of its adequacy in measuring volatility as used in most empirical literature [21, 22-26]. The main reason is that oil prices are subject to random movements, and failure to measure volatility may result in spurious regression; hence the use of the GARCH model. Following [26], the GARCH (1, 1) model takes the following form:
Yt =9o +V,,
where Yt denotes crude oil market volatility series; ~ N(0, o2t)
a2t = ro + ae _i + $ht _i,
2
e, =a+c ,
1 URL: www.cbn.gov.ng
2 URL: www.opec.org
3 URL: http://www.matteoicoviello.com/gpr.htm
Table 1
Descriptive statistic results
OILPV CONF OPEC GDP EXR INFL
Mean 39.42000 50.82636 78.21818 86.27848 63.43879 68.49970
Median 9.700000 49.49000 15.10000 102.1000 37.74000 19.80000
Maximum 279.8000 145.2900 450.0000 161.9300 200.0700 157.5000
Minimum -2.340000 16.60000 -12.40000 7.980000 6.130000 10.80000
Std. Dev. 80.61406 32.86887 124.9454 56.92622 57.05584 59.51063
Skewness 2.364066 0.836726 1.782341 -0.198368 0.661863 0.307081
Kurtosis 6.937446 3.158982 5.050664 1.418925 2.276235 1.273690
Jarque-Bera 52.05572 3.885356 23.25425 3.653649 3.129618 4.616344
Source: The author's computations.
Table 2 Results of GARCH (1,1)
Variable Coeff. Std. Error z-Statistic Prob.
Mean Equation C*** 0.085890 0.338521 0.253721 0.7997
oilpv(-l)*** 2.219443 0.810438 Variance Equation 2.738571 0.0062
C* 1.131363 0.305707 3.700814 0.0009
ARCH* -0.174157 0.090197 -1.930855 0.0535
GARCH(-l)*** 0.043771 0.071363 Diagnostic Test ARCH-LM 0.613363 0.5396
Obs.RA2 -0.107611 0.5557
Notes: ", "" and """ explain 10%, 5% and 1% significance level. Source: The author's computations.
where o2t_1 represents the GARCH term; rn and ht are the conditional mean and variance of the crude oil market.
Similar to [14], we the use crude oil price volatility index as the dependent variable, which is regressed on conflict, proxy by the geopolitical risk index, OPEC production, GDP growth, exchange rate, and inflation rate. We employ the autoregressive distributed lag (ARDL) model to estimate the equation. Thus, we specify our model in the following form:
OILPV = f(CONF, OPEC, GDP, EXR, INFL).
The econometric specification of the model can be written as follows:
OILPV= 5 0 + 15 ^ONF + S 2OPEC + 5 3GDP + + 5 4EXR + 5 5INFL + S.
The Auto-regressive Distributed Lag (ARDL) model takes the following form:
OILPV = X 0 + 1CONFt_t +Y52OPECt _ + 53 GDPt_ + 154 EX^ + 55INFLt_ + 56ecm + et,
where OILPV stands for oil price volatility index measured by Brent oil price, CONF is conflict measured by geopolitical risk index, OPEC is the OPEC production quotas, GDP is growth rate of GDP, EXR is nominal exchange rate (naira/US dollar), INFL is inflation rate, while À0 is the intercept, 51 - ô5 are coefficients of
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i—r 12
-1-1-r
14 16
-1-1-r
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22
Fig. 2. Conditional variance of oil price volatility series
Source: Compiled by the author.
the independent variables and St is a vector white noise.
Results and discussion
The results of the descriptive statistics are presented in Table 1. In Table 2, we present the result of the GARCH (1, 1) model and its diagnostic test. We proceed to calculate the volatility series (oilpv), by taking the first difference of the logarithm of the oil price [27-29]. The oil price volatility series is then tested for stationarity in line with [30]. We observed that the crude oil price is stationary in its level form using ADF and PP tests. As indicated in Table 2, the fact that the sum of the ARCH and GARCH coefficients are close to unity suggests that wars, conflicts, and other geopolitical events make the oil market more volatile. The diagnostic procedures of the GARCH (1, 1) in Table 2 indicate that the mean and variance equations are correctly specified, and there is no problem of serial correlation, as reflected by the low probability values. Furthermore, the LM test demonstrates the absence of ARCH effect. Fig. 2 plots the volatility series. The oil price volatility series over this time period show that changes in oil prices are persistent [31].
Our descriptive statistics in Table 1 indicate the existence of negative asymmetry. The fact that Jarque-Bera values are greater than their
Kurtosis values further demonstrates that the series are normally distributed. In addition, stationarity test results in Table 3 show that the series CONF, OPEC, GDP, and EXR are stationary at order I(1), whereas OILPV and INFL are stationary at I(0). This means that the variables' levels of integration vary, as demonstrated by the ADF and PP unit root tests. Given the mixed order of integration in this instance, ARDL is preferable. Furthermore, to determine whether the variables used in this study have any long-term relationships, we used the bounds co-integration test. The computed F-statistic exceeds the critical value, as shown in Table 4. This indicates that stable long-term relationships exist among the variables.
The estimated results of the ARDL model are presented in Table 5. The short-run estimates show that wars, conflicts and other geopolitical events contribute to the volatility of crude oil prices. Our finding reveals a significant positive correlation between the geopolitical tensions in Nigeria and the volatility of the crude oil market, which is in line with the findings of [17, 19, 21-23].
OPEC oil production is found to be significant in explaining the dynamics of prices during the period of study, supporting findings by [14, 16]. The finding indicates that the influence of OPEC production quotas on
Table 3 Unit root test
Variable ADF Remark PP Remark
GDP -4.64* I(1) -3.62* I(1)
OILPV -3.25*** I(0) -4 39*** I(0)
GPR -5.36*** I(1) -5.36*** I(1)
TOP -4 53*** I(1) -4.53*** I(1)
INFL -3.51** I(1) -4.65** I(1)
EXR -3.64** I(0) -3.75** I(0)
Notes: *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Source: The author's computations.
Table 4
Bounds test results
Model
K
Computed F-statistics
Remarks
(F(OILPV, CONF, OPEC, GDP, EXR, INFL))***
K
5
5
10% I(0) I(1)
4.271478 5%
I(0) I(1)
reject H0 1%
I(0) I(1)
2.08 3.00 2.39 3.38 3.06 4.14
Note: *** shows 1% significance level. Source: The author's computations.
oil price volatility is positive and significant. This means that an increase in OPEC oil production would lead to a decline in crude oil price in international market, while a cut in OPEC production quotas leads to an increase in price.
Our analysis also reveals that oil price volatility slows down growth, as demonstrated by the negative value of GDP. The estimated coefficient of error correction term of -0.26 was significant at the five percent level, indicating that about 26 percent of any disequilibrium would be easily corrected in a short period of time.
The long-term association between OPEC production quotas and crude oil price is positive and significant, meaning that excess supply and production of crude oil by OPEC lead to surpluses and reductions in price, while a reduction in OPEC production has the poten-
tial to drive prices up. The import of this analysis is that shocks in oil production by OPEC can trigger higher oil price volatility. Our result also indicates that oil price shocks trigger inflation in Nigeria. A change in oil prices causes inflation to increase by 1.85 percent. The economic growth proxy by GDP growth rate was significantly and negatively impacted by oil price volatility. This result implies that OILPV slows growth over the long term. Additionally, OILPV relates negatively to the exchange rate. This means that over time, OILV would lead to a depreciation of the exchange rate.
The post-estimation test findings in Table 6 show that the model does not have an autocorrelation problem because the probability value of the serial correlation LM test is 0.79, which is higher than 0.05. The model also does not have a heteroskedasticity issue. We also
Table 5
Estimated ARDL model
Dependent Variable: OILPV
Variables Coefficient t-Statistic Prob.
Short-run
CONF(-1)* 0.465875 2.300903 0.0352
D(OPEC)** 2.802577 7.019782 0.0000
D(OPEC(-1))** 1.032950 2.958014 0.0093
D(GDP)** 0.062744 2.359158 0.0400
D(GDP(-1))** -3.380960 -5.030246 0.0005
D(EXR)** -0.773321 -1.990892 0.0639
D(EXR(-1))** 1.843255 2.402754 0.0371
D(INFL)** 0.831096 2.231574 0.0403
ECT*** -0.269204 -10.84409 0.0000
Long-run
CONF* 1.061804 2.043539 0.0578
OPEC*** 1.520060 2.275624 0.0370
GDP** 0.062744 2.359158 0.0400
EXR*** -0.538709 -4.894435 0.0006
INFL** 1.858986 7.219760 0.0000
C*** -57.07929 -1.927503 0.0719
Diagnostic test
Serial correlation 0.79P
Heteroscedasticity 0.55P
Specification bias 075P
Notes: *, ** and *** explain 10%, 5% and 1% significance levels respectively; p indicates F-Statistic Probability. Source: Author's computations.
discover that the model does not suffer mis-specification bias.
We ran the CUSUM and CUSUMSO in order to confirm the model's stability. Figs. 3 and 4 illustrate the results. The stability of the model is shown by the graphs of CUSUM and CUSUMSO. None of the recursive residuals were outside the two critical lines; all are inside the 5% critical lines.
Conclusion
This study examined the volatility risk of the crude oil market and macroeconomic con-
ditions in Nigeria using monthly data from January 2012 to December 2022. We used the GARCH (1, 1) model to estimate the volatility of the oil market and the ARDL method to analyze the data. We discovered that conflicts in Nigeria and OPEC oil production are significant in explaining the dynamics of the price of oil during the study period. Oil price volatility has also led to a high inflation rate, exchange rate depreciation and slowed down GDP. The paper concludes that oil price volatility significantly influences inflation and exchange rate depreciation. The alarming
Fig. 3. CUSUM
Source: Author's computations.
Fig. 4. CUSUMSQ
Source: The author's computations.
inflation rate in Nigeria is attributed to the sharp increase in oil prices.
Our findings have long-term policy implications for the Nigerian economy. Oil price volatility has heightened inflation pressures, causing spikes in energy and commodity prices, depreciation of currency, and a depressing long-term growth prospect.
This paper suggests that the Nigerian government and policymakers need to strongly adopt policy measures that would increase energy efficiency and lessen the country's dependency on oil exports through diversification of its export base to hedge against oil volatility; otherwise, the economy would deteriorate and crumble eventually as the volatility persists.
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ABOUT THE AUTHOR / ИНФОРМАЦИЯ ОБ АВТОРЕ
Nuhu Musa — Ph.D. (Economics), Senior Lecturer, Department of Economics, Faculty of Social Sciences, Kogi State University, Anyigba, Nigeria
Нуху Муса — Ph.D. (экономика), старший преподаватель кафедры экономики факультета социальных наук, Университет штата Коги, Аньигба, Нигерия http://orcid.org/0002-6202-0969 musanuhuadams@gmail.com
Conflicts of Interest Statement: The author has no conflicts of interest to declare.
The article was submitted on 23.08.2023; revised on 03.11.2023 and accepted for publication on
20.11.2023.
The author read and approved the final version of the manuscript.
