The Effect Of Foreign Exchange Rate Volatility On The Financial Performance Of Commercial Banks In Kenya
Realized volatility is a measure of actual price volatility, based on past prices over a given time period. It is sometimes called historical volatility or historical deviation. Realized volatility stands in contrast to implied volatility, which is based on expectations of future price movement. Realized volatility can be calculated and expressed in different ways, such as in terms of indexes Kenya like many other developing countries has adopted a floating rate regime which means that the price of the Kenya Shilling (Kshs.) with respect to other currencies is set by market forces of demand and supply. As per commercial banks’ foreign exchange trading activities audit by Central Bank of Kenya (CBK) in 2011, the main drivers responsible for the increase in the level of activities during this period were identified as “reverse carry” deals, shortening of the tenor of currency swaps, the preference by Kenyans to hold their wealth in foreign currency and the use of Electronic Brokerage System (EBS) for foreign exchange trading. The research method that was adopted in this research is the quantitative method since the main concern was the relationships between the variables and analysis of the causal using numerical data and statistics. The quantitative method focuses on the measurement and analysis of causal and effect relationship between variables. Exchange rate volatility was looked at by establishing the daily ratio of the day’s exchange rate to the previous day. The study findings showed that there was high volatility in foreign exchange rate within the first quarter of 2008 (January and March). The volatility reduced, almost evened out between the second and third quarter of the year before increasing in the beginning of the third quarter. Moreover the study used tolerance and Variance Inflation Factor (VIF) values for the predictors as a check for multicollinearity. Tolerance indicates the percent of variance in the independent variable that cannot be accounted for by the other independent variable while VIF is the inverse of tolerance. Finally, the study showed that tolerance values ranged between 0.221 and 0.633 while VIF values ranged between 1.579 and 4.526. Since tolerance values were above 0.1 and VIF below 10, then were was no multicollinearity in the model. The study recommends that since there are several economic implications of these results for both business policy and public policy. Firstly, when the market is inefficient in processing information, it implies that there are significant lags between dissemination of information and market participant’s reaction to news. These information lags could arise from market segmentation and/or poor utilization of information communication technologies in the market. Therefore, to improve the information efficiency in the foreign exchange market, the government should consider using information technology infrastructure to provide information on exchange rates to the wider public. This is already happening with respect to the stock market. For individuals and businesses, this implies that they can profitably utilize their sophisticated IT infrastructure to gather information and exploit it to earn profits in the foreign exchange market. Secondly, persistence and nonlinearity in volatility also suggest inefficient information processing in the market. This could be attributed to irrationality or heterogeneous expectations or risk aversion in the market that causes participants to herd together. Therefore, the CBK needs to intervene in the market to reduce information asymmetry and speculation, which could be contributing to nonlinearity and persistence in volatility.