**4. Data interpretation**

The descriptive statistics is made up of statistical distribution of a total of 136 observation presented in **Table 2**. The result showed that almost all the variables demonstrated significant level of consistency within the minimum and maximum values. The closeness of the mean and median indicates the variables have a normal distribution. It was discovered that the mean and the median values are positive Also, the mean and the median falls within the maximum value. This suggest that the individual values are normally distributed. The large difference between the maximum and minimum exchange rate indicates the high volatile nature of exchange rate. The standard deviation which measures the amount of dispersion of a variable revealed that the standard deviation of the variables reported were very low except for trade balance and interest rate which are greater than two. This revealed that the values of the variables clustered around their average with little or no variability. For a normal distribution, the skewness is zero while positive and negative skewness indicates distribution with right and left tail respectively. The standard deviation, skewness and kurtosis greater than zero indicates that the distribution is not normally distributed. The positive skewness of exchange rate returns, industrial output and interest rate implies their distributions are skewed to the right. Then, the negative skewness of exchange rate, trade balance and inflation indicate their distribution are skewed to the left. Negative skewness denotes a tail towards the left side of the distribution while positive skewness signifies a tail towards the right side of the

*Exchange Rate Volatility and Macroeconomic Performance in Nigeria DOI: http://dx.doi.org/10.5772/intechopen.100444*


*Note: NER is exchange rate; RNER is the returns of exchange rate,TDB is trade balance; IDP is industrial output; INF is inflation, INR is interest rate.*

*Source: Authors computation 2021.*

#### **Table 2.**

*Descriptive statistics.*

distribution. Lastly, the Jarque-Bera statistics which is used to test the normality in the distribution of the series. The result indicated that the probability of J-B is or less than 0.05. Therefore, the null hypothesis of normal distribution is rejected and the alternative hypothesis that the variables have a non-normal distribution is accepted. The implication of the non-normality of trade balance and inflation verified their responses to the volatile nature of exchange rate movement in Nigeria.

The result of the unit root test presented in **Table 3** was to verify the stationarity properties of the variables. The Augmented Dickey Fuller (ADF) and Phillips Peron (PP) test was used to determine the order of integration of the variables. The result revealed that exchange rate returns (RNER) and trade balance (TDB) are stationary at levels, i.e., I (0) while NER (exchange rate), industrial output (IDP), inflation (INF) and interest rate (INR) are stationary at first difference, i.e., I (1), hence the variables are integrated at levels and order one i.e., mixture of I (0) and I (1).


*Note: \*\*\*, \*\*, \* at 1%, 5% and 10% respectively.*

*Source: Authors computation 2021.*

#### **Table 3.** *Unit root test.*

### **4.1 Preliminary test**

In order to verify the presence of heteroscedasticity in the model, ARCH test was carried out using Breush-Pagan-Godfrey method. Therein, the evidence of ARCH effect enables the study to proceed to estimating non-linear GARCH model. The ARCH effect result is presented in **Table 1** while **Figure 1** shows the evidence of volatility clustering. Therein, the null hypothesis states that there is no presence of heteroscedasticity in the return series. But since the probability is 1%, the null hypothesis is rejected while the alternative hypothesis of presence of ARCH effect is accepted.

#### **4.2 Non-linear GARCH results**

The result of EGARCH (1,1) model for dependent variable trade balance is presented in **Table 4**. The normal distribution analysis reveals that the effect of exchange rate volatility on trade balance is negative at 0.013%. The ARCH result is 1.32% at a significance level of 1%, this showed a positive relationship between exchange rate volatility and trade balance. The leverage term is 0.045% which is significant at 1%. The negative leverage term implied that bad news prevails over good news in the foreign exchange market. Likewise, the GARCH result at 0.81% indicate that present volatility positively predicts future volatility at 1% level of significance. The result of student-t distribution shows the marginal effect of exchange rate volatility on trade balance is 0.02%. The result of ARCH is 1.32% at 1% significance which indicates a positive relationship between exchange rate volatility and trade balance. The leverage term at 0.07% was significant at 1%. The positive leverage effect implies that good news prevails over bad news in the foreign exchange market. Also, the GARCH term was 0.81% which indicates that present volatility positively predicts the future volatility at 1% level of significance. Lastly, the generalised error distribution result show that the marginal effect of exchange rate volatility on trade balance is 0.04%. The outcome of the ARCH result is 1.29% and this implies a positive relationship between exchange rate volatility and trade balance. The leverage effect is at 0.183% and it show that goods news prevails over bad news in the foreign exchange market. The GARCH term is 0.80% and this implies that present volatility predicts the future volatility at 1% significance level. Nevertheless, the best model for the distribution is the generalised error distribution with minimum variance of Schwarz Criterion value at 3.5145 and log likelihood value of 212.7013.


#### **Table 4.**

*EGARCH (1,1) result for dependent variable trade balance.*


*Source: Authors computation 2021.*

#### **Table 5.**

*EGARCH (1,1) result for industrial dependent variable output.*


*Source: Authors computation 2021.*

#### **Table 6.**

*EGARCH (1,1) result for dependent variable inflation.*

The result of EGARCH (1,1) for dependent variable industrial output is presented in **Table 5**. The outcome of the normal distribution show that the effect of exchange rate volatility on industrial output is positive at 0.04%. The leverage term is 0.95% which indicates that good news prevails over bad news in the foreign exchange rate market. The positive outcome of ARCH is at 0.93% with a significant level of 1%, this shows the existence of a positive relationship between exchange rate volatility and industrial output. The GARCH result is 0.94% at 1% significance level and this indicates that present volatility positively predicts the future volatility at 1% level of significance. The student-t distribution result reveal that the marginal effect of exchange rate volatility on industrial output is 0.04% at 10% significance level. This indicate that exchange rate volatility exhibits a positive relationship with industrial output in Nigeria. The leverage effect is 0.08% which indicate goods news prevails over bed news in the foreign exchange market. The ARCH result of 0.08% infer that present volatility positively predicts the future volatility at 1% significance level. Moreover, the GARCH term at 0.94% infer that present volatility positively predicts the future volatility at 1% significance. The result of generalised error distribution showed that the effect of exchange rate volatility on industrial output is 0.04%. This designates a positive relationship between exchange rate volatility and industrial output in Nigeria. The leverage term at 0.09% indicate that good news prevails over bad news in foreign exchange market. In reporting the GARCH term at 0.93%, this infers that present volatility predicts future volatility at 1% significance. In relating the distributions in terms of goodness of fit, normal distribution is the best model to be considered with a minimum variance value of SC at 0.5102 and the log likelihood maximum value at 12.3675.

The EGARCH (1,1) result for dependent variable inflation is presented in **Table 6**. The normal distribution show that the effect of exchange rate volatility on inflation is negative at 0.03%. The ARCH result at 1.0% with a significant level at 1% infer that exchange rate volatility and inflation are positively related. The leverage term is 0.45% and indicates that bad news prevails over good news in the foreign exchange rate market. However, the GARCH effect is 0.79% which indicates that the present volatility predicts the future volatility at 1% significance level. The student-t result revealed that the marginal effect of exchange rate volatility on inflation is negative at 0.03%. The ARCH result is 1.01% and significant at 1% level indicate the existence of a positive relationship between exchange rate volatility and inflation. The leverage effect at 0.44% infer that bad news prevails over good news in the foreign exchange market. The GARCH term at 0.81% revealing that present volatility predicts the future volatility at 1% level of significance. Lastly, the generalised error distribution result showed that the marginal effect of exchange rate volatility on inflation is negative at 0.03%. The ARCH effect at 0.99% deduce there is a positive relationship between exchange rate volatility and inflation at 1% significant level. The leverage term at 0–0.48% indicate that bad news prevails over good news in the foreign exchange market. Then the GARCH result at 0.79% indicate that present volatility predicts future volatility at 1% level of significance. In choosing the best model for goodness of fit with minimum variance, the normal distribution with log likelihood value of 83.9817 and minimum variance of 1.7712 is selected.

### **5. Conclusion and policy implication**

The study examined the asymmetric relationship between exchange rate volatility and macroeconomic performance in Nigeria through the period between 1986Q1 and 2019Q4 using the non-linear GARCH model. In order to employ the

*Exchange Rate Volatility and Macroeconomic Performance in Nigeria DOI: http://dx.doi.org/10.5772/intechopen.100444*

non-linear GARCH model (EGARCH), ARCH effect was verified to confirm the presence of heteroscedasticity. The preliminary test such as unit root test and ARCH test to verify the presence of heteroscedasticity revealed the evidence of volatility clustering. The outcome of the analysis revealed that volatility movement was very high and persistent over the period of study. Exchange rate volatility exhibited a positive relationship with trade balance and industrial output but a negative relationship with inflation. The leverage effect of exchange rate volatility on trade balance and industrial output indicated the prevalence of good news over bad news. But contrary to the aforementioned, the leverage effect of exchange rate volatility on inflation revealed the prevalence of bad news over good news in the foreign exchange market. On the other hand, the present volatility predicts the future volatility for all the variables. Also, the non-linear GARCH model confirmed that the normal distribution is the best model for traders in the financial market. The implication of the findings imply that exchange rate volatility is an important factor in the macroeconomic performance of the Nigerian economy. It is therefore recommended that government should procure a stable economy through the monetary authorities (CBN) by implementing policies to control and regulate exchange rate and macroeconomic variables in order to control the general price level. Also, investors and financial analyst should consider exchange rate volatility in predicting macroeconomic performance in the future by ensuring stability in the financial market. Furthermore, even though investors in financial markets are risk averse, it is recommended that they should be more sensitive to good news rather than bad news in the events of movement of exchange rate volatility on macroeconomic performance in Nigeria.
