**Table 7.**

*Estimated VEC model 1.*

#### *Exchange Rate Volatility and Monetary Policy Shocks DOI: http://dx.doi.org/10.5772/intechopen.99606*


#### **Table 8.**

*Estimated VEC model 2.*


### *Exchange Rate Volatility and Monetary Policy Shocks DOI: http://dx.doi.org/10.5772/intechopen.99606*


*Notes: Standard errors are in ()and t-statistics are in [].*

#### **Table 9.**

*Estimated VEC model 2.*

exchange rate volatility in the short run. The results lead to the acceptance of null hypothesis that the treasury bills rate and the liquidity ratio have no significant nexus with the volatility in the exchange rate in the, but the impact of exchange rate on exchange rate volatility is significant in the short run, thus rejection of the null hypothesis concerning this nexus.

#### *4.1.4 Impulse response functions*

The results of the Impulse Response Functions (IRFs) of the first VEC model in graphical form are reported in the **Figure 1**.

The impulse response function of exchange rate volatility in the VEC model to a shock in itself shows that exchange rate volatility reacted positively to its innovations all through the five months of forecast. Similarly, it responded positively to innovations in monetary policy rate throughout the five months of forecast. However, exchange rate volatility responded negatively to a shock in money supply all through the five months of forecast.

**Figure 1.** *Impulse response functions. Source: Results extract from E-views 11.0*

**Figure 2.** *Forecast error decomposition functions. Source: Results extract from E-views 11.0.*

### *4.1.5 Forecast error variance decomposition functions*

The estimation of a VAR model firstly requires the explicit choice of lag length in the equations of the model. In this study, graphs of the forecast error variance decomposition functions are presented in **Figure 2**.

As shown in **Figure 2**, it can be observed from the variance decomposition of exchange rate volatility that own shocks contributed most [100%] to its variations in the first month of forecast but declined marginally to 91% in the fifth month of forecast. The proportions of variations in exchange rate volatility, due to shocks in monetary policy rate and money supply, are minimal throughout the five months of forecast. The two variables accounted for less than 6% in the entire periods of forecast. Hence, the variance decomposition of exchange rate volatility shows that own shocks predominantly determined variations in its variations throughout the months of forecast. Monetary policy rate and money supply accounted for less than an average of 6% variations in exchange rate volatility all through the five months of forecast. The proportions of variations in exchange rate volatility due to money supply shocks are marginally higher than those of monetary policy rate during the periods.

#### *4.1.6 Impulse response functions*

The results of the Impulse Response Functions (IRFs) of the second VEC model in graphical form are reported in the **Figure 3**.

The impulse response function of exchange rate volatility in the VEC model to a shock in itself shows that exchange rate volatility reacted positively to its innovations all through the five months of forecast. However, it responded negatively to innovations in treasury bill rate throughout the 5 months of forecast. With respect to a shock in liquidity ratio, exchange rate volatility responded positively all through the 5 months of forecast. Similarly, exchange rate volatility responded positively to shocks in exchange rate within the 5 months of forecast.
