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Oil price shocks and Nigeria’s
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Nigeria's economic engine, and it has always determined the country's economic course, as public finances have always been focused on the oil price benchmark. According to a CBN annual report in 2018, oil revenue led the way, accounting for 79.9 percent in 2011, 69.8 percent in 2013, 67.5 percent in 2014, 55.4 percent in 2015, 48.0 percent in 2016, 52.6 percent in 2017, and 58.1 percent in 2018. This fact implies that the economy's stability is inextricably linked to the oil market, making it vulnerable to shocks. Furthermore, according to a 2019 study by the International Monetary Fund, Nigeria's economy shrank by 0.4 percent and 2.06 percent in the first and second quarters of 2014, respectively resulting in oil price fluctuation. This also demonstrates that Nigeria's GDP and government receipts are strongly correlated with oil prices.
Considering the number of studies on oil prices and Nigeria‟s stock market, for example, According to Effiong (2014), Nigeria‟s stock market reaction to oil supply shocks is insignificantly negative, but aggregate demand and oil-specific demand shocks are substantially positive. Findings of Babatunde, Adenikinju, and Adenikinju (2013), reveals that stock market returns react positively to oil price shocks for a short period of time before reverting to negative results, depending on the nature of the oil price shocks. Also, empirical evidence from Awolaja and Musa (2017) show that the effect of oil prices on stock prices is symmetric in the short run and asymmetric in the long run. Because of the symmetry, a given magnitude of positive and negative oil price shocks would have the same effect on the oil and gas stock market index. This paper, similar to Effiong (2014) employed a structural vector autoregressive (VAR) model to investigate the relationship between oil price shocks introduced in Kilian (2009) and Nigeria‟s stock market. However, the current study differs from the aforementioned in that monetary policy rate and money supply are included in the structural VAR model to capture the possible effects of monetary policy on the relationship between oil price shocks and the Nigerian stock market. To the best of our knowledge, this is the first research that considers the influence of monetary policy and Kilian (2009) identified oil shocks to Nigeria‟s stock market.
Data and Methodology
Data
The variables in this study are grouped into three classes. The first class include the global crude oil production (Q) measured in million barrels, global economic activity (GEA) expressed in percent and the price of Brent (PB) which is in US dollars. The second class include the monetary policy rate (MPR), and the broad money supply (M2) and the third class include the stock returns (SR) of the Nigerian stock exchange all measured in percentages.
The frequency of the data is monthly starting from January 2001 to December 2018. The first class of variables were sourced from the U.S. Energy Information Administration (EIA), except for the global economic activity (GEA) that is developed by Kilian (2009). The second and third class were obtained from the Central Bank of Nigeria (CBN).
Structural VAR specification
Following Kilian (2009), a structural VAR (SVAR) is used to model the endogenous relationship between oil price shocks on Nigeria‟s stock returns, allowing for interaction between the variables and control for monetary policy. The monthly VAR specification is thus given by;
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Where and are coefficient matrices is a vector of uncorrelated structural shocks, is the lag criteria and is a vector of endogenous variables comprising of global crude oil production, global economic activity, the price of Brent, monetary policy rate, broad money supply and stock returns, considering that the ordering of the variables is significant. That is, stock returns account for feedback from all variables above it. This is often referred to as the Wold causal chain (Wold 1960) and is based on traditional literature approach. (e.g. Kilian and Lewis 2011). A lag2 criteria of 12 and an intercept is used in the reduced SVAR, enabling for probably lengthy delays in influences of oil shocks on other variables. The variance-covariance matrix of the structural shocks is normalized such that
This means no cross-equation co-variances in the residuals. To estimate the structural model the reduced form is obtained by multiplying both sides of the structural VAR in equation 1 by
given;
Where and
The reduced form errors are linear combinations of the structural errors , with variance-covariance matrix . The estimate is based on Cholesky decomposition and stock returns responds to the shocks contemporaneously. Therefore, the recursive short-run restrictions are specified as:
SR MS MPR
shock demand
specific oil
shock demand
aggregate
shock ply
oil
b b b b b b
b b b b b
b b b b
b b b
b b b
SR MS MPR
PB GEA
Q
t t t t
t t
ut ut ut
ut ut
ut
_ _
_ _
_ sup _
0 0 0
0 0 0
0 0 0 0
0 0 0 0 0
66 65 64 63 62 61
55 54 53 52 51
44 43 42 41
33 32 31
22 21 11
2The Schwarz criterion (SC) and Hannan-Quinn information criterion (HQ) recommend a lag of 1, while the Akaike in- formation criterion (AIC) recommend a lag of 2. However, identical to Kilian (2009), Wei and Guo (2017), neither is chosen because it does not fully grasp the delays of oil shocks on the stock market. Also, in L tkepohl (2006), the above criteria are primary used for forecasting and may not be ideal for impulse response.
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Results and Discussion
Table 1: Unit root tests at first difference
Variables ADF (t test) ADF (p-value) PP (t-test) PP (p-value)
Q -13.03986 0.0000 -15.01740 0.0000
REA -10.88022 0.0000 -10.42821 0.0001
PB -11.11981 0.0000 -11.08915 0.0000
MPR -18.46944 0.0000 -18.02613 0.0000
MS -14.62130 0.0000 -14.62012 0.0000
SR -11.85594 0.0000 -90.58749 0.0001
Source: Authors computation using Eviews 10
In Table 1, the Augmented Dickey-Fuller test and the Philips-Perron tests shows the variables under study have no unit root at first difference. This means the variables are stationary at first difference and integrated of order one.
Full sample estimate
In this part, the impulse response for the entire sample is reported. Here, it is assumed that one standard deviation of the sample for oil production, global economic activity and price of oil allows for comparisons of the variables over different simulation. In Fig. 1 the response of the monetary policy is explained. First, the monetary policy rate responds significantly to the three types of shocks at different periods. Precisely, the policy rate responds significantly positive to oil supply shocks after the first five months with a decrease in the sixth and tenth months. On contrary, it declines significantly after the first five months in response to aggregate demand shock while it responds sluggishly weak in the first seven months to oil-specific demand shock and increase slightly in the last quarter. Due to the difference in response of the monetary policy rate to the three types of shocks, the money supply response to the shocks also differ extensively. For instance, the response of the money supply to oil supply shock is significantly decreasing, but the response of money supply to aggregate demand shock is positively weak in all periods. However, response to oil-specific demand shock is significantly positive in all periods. Lastly, the response of the stock return to the three shocks is illustrated. Response of stock returns to oil supply shock is sluggish and weak in all periods. Stock returns response to aggregate demand shock is positive in the first five months, it then decreases in the seventh month. This suggests that increase in global economic activity and high demand for crude oil leads to higher oil prices and stock market returns, resulting in a transfer of wealth to exporting countries. While the response to oil-specific demand shock is significantly decreasing in the second and third quarter with much deeper effect. This shows that shocks related with movements in precautionary demand for oil also impact stock market returns. This inference is closely aligned with Kang and Ratti (2015).
Dutse International Journal of Social and Economic Research Vol. 6, No. 3 July 2021
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Oil supply shock
Monetary policy rate Money supply Stock return
.000 .002 .004 .006 .008
1 2 3 4 5 6 7 8 9 10 11 12
Response of LQ to LQ
-15 -10 -5 0 5
1 2 3 4 5 6 7 8 9 10 11 12
Response of REA to LQ
-.10 -.08 -.06 -.04 -.02 .00 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of LPB to LQ
-.4 -.2 .0 .2 .4
1 2 3 4 5 6 7 8 9 10 11 12
Response of MPR to LQ
-.03 -.02 -.01 .00 .01
1 2 3 4 5 6 7 8 9 10 11 12
Response of LM2 to LQ
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of R to LQ Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.
.000 .002 .004 .006
1 2 3 4 5 6 7 8 9 10 11 12
-15 -10 -5 0 5
1 2 3 4 5 6 7 8 9 10 11 12
-.10 -.08 -.06 -.04 -.02 .00 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of LPB to LQ
-.4 -.2 .0 .2 .4
1 2 3 4 5 6 7 8 9 10 11 12
Response of MPR to LQ
-.03 -.02 -.01 .00 .01
1 2 3 4 5 6 7 8 9 10 11 12
Response of LM2 to LQ
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of R to LQ .000
.002 .004 .006 .008
1 2 3 4 5 6 7 8 9 10 11 12
-15 -10 -5 0 5
1 2 3 4 5 6 7 8 9 10 11 12
-.10 -.08 -.06 -.04 -.02 .00 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of LPB to LQ
-.4 -.2 .0 .2 .4
1 2 3 4 5 6 7 8 9 10 11 12
Response of MPR to LQ
-.04 -.03 -.02 -.01 .00 .01
1 2 3 4 5 6 7 8 9 10 11 12
Response of LM2 to LQ
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of R to LQ
Aggregate demand shock
Monetary policy rate Money supply Stock return
-.002 .000 .002 .004 .006
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LQ to REA
-5 0 5 10 15 20
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of REA to REA
-.02 .00 .02 .04 .06 .08
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LPB to REA
-.4 -.2 .0 .2
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of MPR to REA
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LM2 to REA
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of R to REA Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.
-.002 .000 .002 .004 .006
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LQ to REA
-5 0 5 10 15 20
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of REA to REA
-.02 .00 .02 .04 .06 .08
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LPB to REA
-.4 -.2 .0 .2
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of MPR to REA
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LM2 to REA
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of R to REA Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.
-.002 .000 .002 .004 .006
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LQ to REA
-5 0 5 10 15 20
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of REA to REA
-.02 .00 .02 .04 .06 .08
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LPB to REA
-.4 -.2 .0 .2
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of MPR to REA
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of LM2 to REA
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Res pons e of R to REA Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.
Oil-specific demand shock
Monetary policy rate Money supply Stock return
-.2 .0 .2 .4
1 2 3 4 5 6 7 8 9 10 11 12
Response of MPR to LPB
-.01 .00 .01 .02 .03
1 2 3 4 5 6 7 8 9 10 11 12
Response of LM2 to LPB
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of R to LPB
Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.
-.2 .0 .2 .4
1 2 3 4 5 6 7 8 9 10 11 12
Response of MPR to LPB
-.01 .00 .01 .02 .03
1 2 3 4 5 6 7 8 9 10 11 12
Response of LM2 to LPB
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of R to LPB
Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.
-.2 .0 .2 .4
1 2 3 4 5 6 7 8 9 10 11 12
Response of MPR to LPB
-.01 .00 .01 .02 .03
1 2 3 4 5 6 7 8 9 10 11 12
Response of LM2 to LPB
-.02 -.01 .00 .01 .02
1 2 3 4 5 6 7 8 9 10 11 12
Response of R to LPB
Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.
Fig. 1: The impulse response function for the full sample period 2001M1-2018M12
Table 2: Variance decomposition of stock returns (%) for the full sample
1month 2month 3month 5month 7month 9month 11month
Q 0.01 3.12 3.01 2.95 3.55 6.25 7.44
REA 0.83 0.82 3.03 3.77 5.12 4.75 5.02
PB 3.46 4.68 4.99 5.57 5.85 7.17 8.18
MPR 0.09 0.90 1.23 1.49 2.47 3.95 4.04
MS 1.92 7.59 7.70 8.54 8.58 7.91 7.79
R 94.42 82.81 80.02 77.66 74.43 69.96 67.51
Source: Authors computation using Eviews 10
Finally, to examine the relationship further, Table 2 illustrate the variance decomposition of the stock market returns for the entire sample period. Oil-specific demand shocks have more effect illustrating the variance in stock market returns. About 5.57% of the variance of stock market returns is influenced by oil-specific demand shocks in the fifth month, 7.17% in the ninth month and 8.18% in the eleventh month. While oil supply shock and aggregate demand shocks have little influence except in the eleventh month. In general, oil-specific demand shock tends to play a bigger role in describing the variance in stock market returns. The
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variance decomposition of stock market returns is in accordance with the results of the impulse response.
Conclusion
In this research, using monthly data, the relationship between oil price shocks identified in Kilian (2009) and Nigeria‟s stock market is illustrated. Based on the sample estimate the following insights are derived. (i.) Aggregate demand shock have more effect on the policy rate while the money supply is positive in all periods responding to oil-specific demand shock. (ii) Stock returns response to aggregate demand shock is positive in the first five months, and it decrease in the seventh month. Stock returns also decreases significantly negative in response to oil-specific demand in the second and third quarter with much deeper effect in the third quarter. Overall, the variance decomposition similar to the impulse response confirmed the role of speculative and precautionary demand for oil in oil shocks.
Findings of the study have significant implications. To begin, determining the fundamental reasons of oil price fluctuations is important and helpful in determining the origin and consequences of oil price shocks on the Nigerian stock market. Second, decision makers should give greater attention to speculative behavior in the oil market to better understand the effects of oil price shocks on the Nigerian stock market.
This empirical evidence is also crucial for stockholders and fund managers who are interested in the Nigerian stock market. For example, according to the study, about 5.57% of the variance of stock market returns is influenced by oil-specific demand shocks in the fifth month, 7.17% in the ninth month and 8.18% in the eleventh month. These findings, help stockholders and fund managers to precisely predict the risks while using exchange market data.
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