Exchange Rate Volatility and the Performance of Manufacturing Sector in Nigeria: An Econometric Analysis

This study examined the impact of exchange rate volatility on the performance of manufacturing sector in Nigeria from 1981 to 2020 using ARCH/GARCH model and Autoregressive Distributed Lag Model (ARDL). The ARCH/GARCH model confirms that there is a high exchange rate volatility which was validated by their coefficients which were positive and statistically significant at 1% level. The Augmented Dickey Fuller (ADF) unit root test results showed that all the variables were stationary at first difference and the Bound test confirmed a long run relationship among the variables. The ARDL results show that exchange rate volatility, interest rate and inflation rate has a negative impact on the performance of manufacturing sector in the long run while import and gross capital formation have a positive effect on manufacturing performance in the long run. Also, exchange rate volatility, gross capital formation and interest rate were found to have a significant impact on manufacturing performance while import and inflation were found to be non-significant. The findings also show that in the short run that volatility in exchange rate is negatively and significantly related to the performance of manufacturing sector in Nigeria. Furthermore, the coefficient of error correction term shows that about 66 percent of the disparity between the actual and the equilibrium value of manufacturing performance is corrected every year. The study concludes that monetary authorities should formulate a policy framework that will be targeted at improving and stabilizing naira exchange rate. Also, Nigerian government should appropriate more funds to the manufacturing sector. Finally, interest on lending should be reduced to barest minimal to encourage investment both locally and internationally. has no significant effect on the growth of the Nigerian economy. Alabi (2014) investigate the effect of real exchange rate fluctuation on Industrial Output in Nigeria using the OLS regression analysis. The results showed a positive bidirectional relationship between exchange rate and output in Nigeria and other resource dependent economies. They conclude that industrial output in Nigeria can be determined by movement in real exchange rate, capital utilization ratio, technology and available foreign exchange. Akinlo and Lawal (2015) investigated the impact of exchange rate on industrial production for the period 1986-2010. The study employed the used of Vector Error Correction Model (VECM). The findings show that there is long run relationship among exchange rate, industrial production index, inflation rate and money supply. The results also show that exchange rate depreciation does not have significant effect on industrial production in the short run; however, in the long run, the results showed that exchange rate depreciation had significant effect on the industrial production in Nigeria. Lawal (2016) examined the effect of exchange rate fluctuations on the performance of Nigerian manufacturing sector for the period 1986-2014 using Autoregressive Distribution Lag (ARDL) model. The findings of the ARDL revealed evidence of long run and short run relationships among the variables under consideration. The result also showed that exchange rate has positive and significant effect on manufacturing sector output. Nwokoro (2017) assessed the effect of exchange rate and interest rates fluctuations on the manufacturing output in Nigeria from the period 1983-2014 using Error Correction Modeling (ECM). The findings showed that exchange rate and interest rates have negative and significant influence on manufacturing Output. Ugwu (2017) investigate the impact of exchange rate fluctuation on manufacturing performance in Nigeria for the period 1986-2016 using Ordinary Least Squares (OLS) technique. The findings revealed that a significant relationship exists between exchange rate fluctuations and manufacturing performance in Nigeria. Adegbemi (2018) examined the effect of the changes in the macroeconomic factors on the manufacturing sector performance in Nigeria for the period 1981-2015. The findings indicated a negative relationship among interest rate, inflation rate, broad money supply, exchange rate and manufacturing performance. The interest rate and inflation rate were found to be statistically non-significant. The result also showed that gross domestic product and unemployment have positive and significant impact manufacturing performance in Nigeria. Falaye


Introduction
Manufacturing sector is one of the key sectors of many economies globally. It is a quest for improvement in production in association to import substitution and creating foreign exchange earnings capacity (Fakiyesi, 2015). Manufacturing is the production of goods through the application of labour, machines and tools. It involves both manpower and high technology through which raw materials are converted into finished product (Adofu, Taiga & Tijani, 2015).
Exchange rate is a vital tool used in the determination of the international competitiveness of a nation. Exchange rate is the rate at which one currency is exchanged for another. Exchange rate is determined by the interaction of demand and supply of foreign exchange. Thus, if demand for a currency rises with the supply being constant, the exchange rate of the currency will appreciate. But if the demand for the currency falls with the supply remaining constant, the exchange rate will depreciate (Ezenwakwelu, 2017).
In an effort to enhance the performance of the manufacturing sector of Nigeria and also deal with other challenges that affects its operations, the Structural Adjustment Programme (SAP) was introduced in 1986. A critical component of SAP was the exchange rate deregulation which was intended to make foreign exchange more accessible for production thereby increasing manufacturing output and employment while reducing inflation (Adegbemi, 2018). The programme envisaged improving the performance of the manufacturing sector by reducing import dependence and promoting manufacturing for export. One of the major objectives of the reform was exchange rate deregulation which allowed exchange rate determination to be market driven (Nwokoro, 2017).
Statistically, exchange rate in Nigeria was 1.75 per USD in 1986 to 8.04 per USD in 1990. It  358.8 per USD in 2020(WDI, 2020. Also, an examination of manufacturing sector share in the GDP in recent years shows that it has not been relatively stable. In 1990, it was about 5.5% while it dropped to 2.22% in 2010. It grew to 7.18% in 2011 to 7.79% in 2013. It further rose to 9.53% in 2015 and to 8.860% in 2018 and further rose to 13.01% in 2020 (WDI, 2020).
The critical challenge faced by Nigerian manufacturing industry is inadequate raw material for production of finished products. This condition tends to affect negatively the productivity level of the sector (Okorontah & Odoemena, 2016). The ability of the manufacturing industry to imports input materials depend on the level of the exchange rates. It is evident that most organisations source their inputs externally. Hence, the devaluation or depreciation of exchange rate tends to impede the performance of the sector (Nsofo, Takson & Ugwuegbe, 2017).
Furthermore, the findings of Akinlo and Adejumo (2014); Lawal (2016) and Oriji et al. (2019) found a positive and significant impact of exchange rate on the performance of manufacturing sector. However, King-George (2013) found that exchange rate volatility has no significant effect on manufacturing sector in Nigeria. The differences in their findings call for further investigation.
Hence, it is against this backdrop that this study seeks to examine the impact of exchange rate volatility on manufacturing sector performance in Nigeria.
The other sections of this paper are arranged as follows; Section two review theoretical and empirical literatures. The third section focuses on methodology. Section four looks at the presentation and analysis of results while the final section provides conclusion and recommendations.

Theoretical Framework
The theoretical framework for the study is Given that the consumption function is; Next substitute into the IS curve to get an expression in Y and i Combining terms with i; And substitute into the IS curve; Simplifying;

Data and Sources
The study employed the use of time series secondary data sourced from the Central Bank of Nigeria (CBN) and National Bureau of Statistics (NBS) between the periods 1981 to 2020.

Estimation Technique
In order to determine the volatility of naira exchange rate for the period under study, the study applied the use of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) Models. The choice of the model is based on the fact that the GARCH model is very reliable in modeling the volatility of financial data.
Finally, to establish the impact of exchange rate volatility on manufacturing performance in Nigeria the study adopted an Autoregressive Distributed Lag (ARDL). However, the ARDL model has difficulties in identifying the relationships between the data variables which contain a unit root as issues of spurious correlation may occur. Therefore, modeling the variables in difference may be used to avoid problem relating to unit root. Finally, the ARDL model is employed to ascertain long-run equilibrium between the variables

ARCH Effect
In order to ascertain whether exchange rate is volatile over the periods of study, the residual from the exchange rate volatility model using the GARCH (1, 1) must satisfy two conditions.
That is, it must have an ARCH effect, and the volatility must be clustered otherwise the variable is not volatile. effect. The findings show that F-statistic is statistically significant at 5% level since the probability value is less than 0.05. This indicates that we reject the null hypothesis and accept alternative hypothesis which stated that there is ARCH effect.  Table 2 shows the correlogram Q-statistics which was also used as a confirmation of the presence of ARCH effect. The findings show that the probability values for AC, PAC and Q-stat are all zeros indicating that the P-value is significant assuming 5% level of significance (P<0.05). This implies the rejection of null hypothesis that stated that there is no ARCH effect.  has a negative sign and statistical significance at 5% level. This shows that there is a negative and significant relationship between exchange rate volatility and the performance of the manufacturing sector in Nigeria. Finally, a close examination on the result shows that the coefficient of ARCH term is higher than that of the GARCH term indicating that volatility in the exchange rate for the periods under consideration tends to be more extreme. The implication of the positive and significant values of the coefficient of ARCH and GARCH term validate the fact that exchange rate volatility of the past periods influence the exchange rate volatility of the current period.

Bound Test Approach to Cointegration
The study applied the ARDL Bounds test to test whether there is a long run relationship among variables. The model has an unrestricted trend with no constant. The results are reported in Table 5 below:

ARDL Model Estimate
Though there is a presence of cointegration, it was necessary to estimate the long-run ARDL in order to calculate the elasticities. Thus, the long run ARDL was estimated or unrestricted ECM was estimated and the results are presented in Table 4 below.    (2) is 0.068 and this is greater than 0.05 at 5% significance level and therefore the null hypothesis is accepted. This implies and therefore confirms the absence of serial correlation.  (6) is 0.3559 and this is greater than 0.05 at 5% significant level and therefore the null hypothesis is accepted. This implies and therefore confirms the absence of heteroscedasticity in the model. In essence, they have constant variance in repeated sampling.

Conclusion
This study examined the impact exchange rate volatility on manufacturing sector performance in Nigeria from 1981 to 2020 using ARCH/GARCH model and Autoregressive Distributed Lag Model (ARDL) as the major statistical technique of analysis. From the findings, the ARCH/GARCH model confirms that there is a high volatility of exchange rate in Nigeria which was validated by their coefficients which were both positive and statistically significant at 5% level of significance. Also, the findings show that there is a negative and significant relationship between exchange rate volatility and the performance of the manufacturing sector in Nigeria.
The ARDL results show that volatility of exchange rate, interest rate and inflation rate has a negative impact on the performance of manufacturing sector in the long run while import and gross capital formation have a positive effect on manufacturing performance in the long run.
Also, exchange rate volatility, gross capital formation and interest rate were found to have a significant impact on manufacturing performance while import and inflation were found to be non-significant. The findings also show that in the short run that volatility in exchange rate is negatively and significantly related to the performance of manufacturing sector in Nigeria. The study concludes that the monetary authorities should formulate a policy framework that will be targeted at improving and stabilizing naira exchange rate. It has been established that manufacturing sector is one of the major key sectors in the many economies of the world.
Therefore, the need for Nigerian government to appropriate more funds to the manufacturing sector should be encouraged. Finally, interest on lending should be reduced to encourage investment both locally and internationally.

Ethics approval and consent to participate
Not applicable

Consent for Publication
Not applicable

Availability of data and materials
The data for this study were sourced from the database of Central Bank of Nigeria (CBN) Statistical Bulletin (https:/www.cbn.gov.ng) and National Bureau of Statistics (NBS).

Competing interests
The author declares that there are no competing interests associated with this manuscript.

Funding
I hereby declare that there was no funding received for this manuscript.

Author's contributions
The author was the overall contributor to the development of all the sections in this manuscript.