Exchange rate is critical in any economy in the world. The variations in exchange rate usually have impact in the global trade. Inflation and exchange rate are positively related, hence decline in exchange rate i.e devaluation of the home currency makes the prices to be high leading to imported inflation in the country [1]. Exchange rate variations are always dominant on monthly basis hence the government has been putting measures to curb the level of inflation. The changes in the monetary factors and the value of a given currency affects inflation, international currency is usually used for trade in most parts of the world, because most of the bank reserves are held in USD makes the dollar international currency. Kenya has been experiencing fluctuation on both exchange rate and inflation over decades, this has led to implementation of several regimes and policies to curb the fluctuations in the economy. Exchange rate in Kenya has had a deviation of 3% monthly between the major currencies in the world [2], recording the highest level at 105KSH/USD in October 2015 while inflation rate in the country has never been stable due to the demand on the necessities, this is experience by the low record of 3.73% and a high end record of 18.31% during the period of under study. This beats the policy measures by the central bank to control the inflation rate of 5% with a variation of 2.5% which aspires to makes the goods and services expensive and slow growth of the economy.

In international economies, exchange rate and inflation have a relationship. A variation in the exchange rate will alter the price level of the commodities hence affecting inflation rate [1]. Further when the shilling becomes stronger than the expectation is that the inflation rate will decline. The Kenya’s government has been continuously putting policies on effect to suite the theory but this has not been the case, for example in January 2012 exchange rate was at 83Ksh/USD while the inflation rate was recorded at 13.31% as a result of increase in the prices of the necessities including food and fuel. On the other hand, in September 2015, the shilling was exchanged at 105Ksh/Usd whereas inflation was recorded at 5.84% in the same month. This signify that exchange rate can affect inflation on indirect channel whereby the producers will be forced to change prices  because of the pressure caused by imported goods [3]. Therefore, the fluctuation on the exchange rate have a significant impact on the inflation rate. The Table 1 which represent definition and measurement of variables.

Table 1: Definition and Measurement of Variables

Kenya aspires to have a shilling control for the competitiveness of the exports. However, the exchange rate has been depreciating to record the lowest value at 105Ksh/USD in September 2015, however the fluctuation also led to recording appreciation of 83.3Ksh/USD in January 2012 which was attributed to increase of the investments from abroad, capital inflows usually affect the level of exchange rate of a given nation [4].

Furthermore, the inflation rate of the country has never been stable with the central bank policy of maintaining it at 5% with the variation of 2.5%. On average the inflation rate was at 9.67% between 2012-2018 with the highest being 18.31% recorded in January 2012 and the Lowest was 3.73% recorded in April 2017. 

Most of the developing countries use exchange rate in economic growth and stabilizing key factors to enhance development. Some targets include minimal inflation and producing goods, which can have a better edge globally. Kenya has been undergoing different regimes on exchange rate due to numerous reasons, for instance BOP crises. Exchange rate is critical in achieving economic goals such as trade balance and inflation. The policy targets and goals have influenced the level of exchange rate in the country. For example, in attaining efficient BOP, then the impact is felt on prices of imported goods. The variation in the exchange rate causes rise in cost of transaction and decline in benefits from global trade, hence, presence of imported inflation. Mwega [4], showed that exchange rate could be used in making the local goods competitive globally and in attaining minimal inflation. However, contradiction exists due to monetary and fiscal policies applied in the country, thus the need for this study to establish the relationship between inflation and exchange rate in Kenya. Muchiri [5] established that in the long run analysis the exchange rate and inflation have a significant relationship. Therefore, this was to determine the relationship in the short run between inflation and exchange rate in Kenya. The monthly data was used to establish the relationship thus enabling the government to consider optimum policies to enhance stability in the economy. The main objective of the study was to establish the relationship among inflation, 

balance of payments and exchange rate in Kenya. Ahmad and Saima [6], determined whether inflation and exchange rate are related in Iran. In the long-run, the adjustments made to cater for price levels fluctuations took long time to be effective. Policies made to curb high inflation were effective for the period of the study. Policies put to cater for inflation variations should be independent of the exchange rate targets. For achievement of targets, emphasise should be on monetary policies. Okoth [7], determined the effect inflation and interest rates have on exchange rate in Kenya. Analysis of data was done using ANOVA and it was established that there existed correlation among interest rates, inflation and exchange rate. Inflation and exchange rate were negatively related. Achieng’ [8], analysed the relationship between exchange rate and inflation in Kenya. Yearly data from 1973 to 2014 was used to establish the relationship. Variables of interested include foreign prices, exchange rate, inflation, interest rate and GDP. The analysis was done using multivariate Vector Auto Regression. In the short-run, there existed a causal relationship between exchange rate and inflation. Muchiri [5] used quarterly data to determine whether inflation and interest rate had effect on foreign exchange rate in Kenya.

Data obtained from 2007 to 2016 and multiple regression analysis was used after relevant diagnostic tests were done. Consumer price index, inflation and money supply had positive effect on foreign exchange rate, FDI and foreign exchange rate were indirectly related. It is expected there exist a relationship between inflation and exchange rate. The relations either positive negative or even negligible. this evidence is enough from the past researches done. Besides the relationship between two variables, there also other factors such as economic growth, balance of payments, foreign direct investment, international debts and interest rates that have the effect positively or negatively. The results intend to facilitate the policy makers to know the efficient policy in helping curbing the high inflation and achieve both internal and external balance.

The purchasing power parity theory postulates that disparity in the exchange rate equals the inflation variations between the countries under consideration. Showing that exchange rate has a relationship with inflation. Considering that the exchange rate and inflation have relationship, it suggests that the past values of the variables will cause the other variables to shift. Hence, would granger cause if occurs before and contains information relevant in the forecasting that is not present in group of other variables. Granger Causality is tested using liner regression model. Consider a bivariate regression model and . The first model to cater for the objective would be as follows. That is relationship between inflation and exchange rate. Where is exchange rate, show inflation rate. is the maximum number of lags and matrix is the coefficients of the model while and are the residual errors? When the variance of E₁ declines when is included in the first equation then granger causes . It is applicable in establishing the relationship between inflation and exchange rate. 

The study used monthly data from various sources, spanning from January 2012 to December 2018.This period was conducive since the economy was stable with the increase in foreign investments in the country, although there were series of events including the elections and natural disasters. Data was sourced from websites of Central Bank of Kenya (CBK) and Kenya National Bureau of Statistics (KNBS). The study took into consideration the average exchange rate in every month while obtaining the direct value for inflation rate from the source. To avoid errors in the analysis, the data were cleansed and refined. Several diagnostic test (unit root test, cointegration test and serial correlation) were done prior to the actual analysis to avoid reporting spurious results. In selection of the optimal lag length, the model employed Akaike Information Criterion (AIC), Hannan-Quinn Information Criterion (H-Q) and Schwarz Information Criterion (SIC) as the appropriate methods of establishing the maximum lag value, Johansen maximum likelihood method for testing cointegration is sensitive to the number of lags. Therefore, the lag length with the maximum lag value was obtained to be two. The ADF was done to avoid spurious results, hence the data were tested for trend and seasonality. The study at both level and difference. The unit roots results with application of AIC for both intercept and intercept and trend showed that inflation was stationary because the ADF test statistic is less than the critical value implying that the identity is of order zero(o). Further, a test was conducted at difference for exchange rate after establishing that it was non stationary at level and it was found that it was stationary at 1^st difference meaning that the null hypothesis for the presence of unit root were rejected at 5 percent for both variables. The study employed the use of Johansen cointegration to determine whether the variables have the relationship in the short run or long run. This was guiding on either to run a VECM or VAR framework. The traces statistic was found to be smaller than the critical value at 5 percent level of significance, with a maximum rank of two. This implied that there exist cointegration of both equations, in either bidirectional or unidirectional relationship, meaning that the dependent and independent variables move closely to achieve a short run equilibrium. Further to establishing the relationship of the variable’s VAR model was used which the variance decomposition for the variables are presented in Table 2. On establishing whether there is a serial correlation between the variables. A pair-wise serial correlation was done and the results obtained was -0.19 which show that inflation and exchange rate have a negative correlation. The diagnostic test was conducted including normality test, Durbin-Watson test, mean of the variables and they are presented in the Table 2. Granger Causality was used to establish whether the previous data of a variable determines the future results of another variable. According to Granger [5], suppose that we have two items G1t and G2t, and that we first attempt to forecast G_t+1 using past terms of G_t and W _t. Therefore, the past of a variable appears to contain information helping in forecasting hence there exist a relationship. From Table 4, it shows that there exists unidirectional relationship between inflation and exchange rate. The first part p-value is 0.7731 which is greater than 0.05 hence the null hypothesis cannot be rejected. Therefore, inflation does not Granger cause exchange rate. The second sub-section of the first part shows a p-value of 0.9067 hence the null hypothesis failed to be rejected. Exchange rate does not Granger cause inflation. Hence, there is non-existence of causality between exchange rate and inflation. The results were contrary to findings by Achieng’ and Muchiri [1,7] which established that there was a positive relationship. The impulse response function (IRF) helps in tracing the effects of each variable in the model for a given period. Impulse Response Function is useful for studying the variable interaction on the vector autoregressive model. The shock of the j^th variable does not only directly affect the j^th variable but is also transmitted to all other endogenous variables through the lag structure. This is the tracing of the effect of the one-time shock to one of the other variables on the current and the future values of the endogenous variable. The impulse response function is presented in (Figure 1).

Table 2: Variance Decomposition Analysis

Table 3: Diagnostic Test

Table 4: Granger Causality Test

Figure 1: Impulse Response Function

The motive of this study was built on the fluctuations of the exchange rate, unpredictable inflation with the negative balance of payments in the country having the assumption that there exist no relationship between the variables. The negative decline in the exports as a result of exchange rate depreciation has a negative impact on economic activity in general, aggregate demand being most affected and later the inflation. This was achieved using the data acquired from Central Bank of Kenya (CBK) and Kenya National Bureau of Statistics (KNBS) with the purpose of establishing the relationship of exchange rate with inflation [9]. 

The study found that there is a standard deviation between the Kenyan shilling and USD of 7.89. Inflation had mean is of 6.83 which is within the range maintained by the central bank of Kenya of 5% with variation of 2.5. The study used the granger causality equation with the aim of achieving the objectives of this study. The objective on the relationship between inflation and exchange rate was empirically found that exchange rate and inflation does not granger cause each other in the short run using the monthly data between 2012 and 2018 with the probability values of 0.77 and 0.90 being greater than the critical values at 5 percent.

The study concludes that in the short run using the monthly data exchange rate does not granger cause inflation, this can be as a result of the short run data. It is also clear that exchange rate had insignificant impact on the inflation and balance of payments hence it is not a suitable variable to monitor the inflation fluctuations and balance of payments crisis in the short run. This finding, suggest that the central bank and the government should design policies which ensure economic growth, this rely the authorities to factor other variables in controlling inflation fluctuations and balance of payment crisis in Kenya. This has the policy implication in achieving the internal and external balance of the country.

1. Ochieng, O. et al. “The Determinants of Inflation in the Kenyan Economy.” International Journal of Economics, 2016, pp. 46–60.

2. Otiato, G. “Shilling Overvalued by 17.5% Says IMF.” Standard Media, 25 October 2018, www.standardmedia.co.ke/business/article/20001300287/shilling-overvalued-by-17-5-per-cent-says-imf.

3. Mussa, M. The Theory of Exchange Rate Determination. University of Chicago Press, 1979.

4. Mwega, F. Real Exchange Rate Misalignment and Implication for Nominal Exchange Rate Level in Kenya. 2012.

5. Muchiri, M. Effect of Inflation and Interest Rate on Foreign Exchange Rates in Kenya. 2017.

6. Ahmad, E., and Saima, A. “Relationship Between Exchange Rate and Inflation.” Pakistan Economic and Social Review, 1999, pp. 139–154.

7. Okoth, N. The Effect of Interest Rate and Inflation Rate on Exchange Rate in Kenya. 2013.

8. Achieng', M. An Empirical Analysis of the Relationship Between Exchange Rates and Inflation in Kenya. 2015.

9. Stockman, A.C. “Theory of Exchange Rate Determination.” Journal of Political Economy, vol. 88, no. 4, 1980, pp. 673–698.