Determinants of variability in foreign exchange risk premium in Kenya

th October, 2014 The overall objective of the study was to assess the determinants of variability in foreign exchange risk premium in Kenya. Specifically, the study sought to assess the effect of fiscal policies on variability of foreign exchange risk Premium; to assess the effect of monetary policies on variability of foreign exchange risk Premium; to assess the perceived effects of each determinant on the variability of foreign exchange risk Premium; and to assess the effect of micro-economic variables on variability of foreign exchange risk Premium. The study employed a descriptive research design. The population of the study comprised of the entire financial markets system, which consists of 204 institutions. The study applied purposive sampling technique to draw data from the floating exchange regime of post 1993 era, selection of financial services and policy experts from within the private and pub lic sector players. A sample of 135 informants was considered representative for the study. The study relied on a mix of primary an d secondary data. A set of data sheets (see appendices) were used as data collection tools. The study envisaged using monthly observations data on the interest rates and foreign exchange rates over the entire sample period of 1993 to 2012. In assessing the determinants of variability in foreign exchange premium in Kenya, the study applied the Stochastic Discount Factor (SDF) methodology. Quantitative data was analyzed using descriptive statistics such as mean, standard deviation, percentages, and multiple regression analysis. Statistical Package for Social Sciences (SPSS) was used to analyze the data. Tables were used t o present summary of the model coefficients and their related descriptive statistics or statistical tests estimates. Scatter plots diagrams and trend line charts were used to show the behaviour of time series data over the sample period. The qualitative data collected through interview guide was analyzed through content analysis. After the models are estimated, the findings were presented using tables, charts, and conclusion drawn there in. The findings of the study were threefold. First, the findings showed that the time series for the fiscal and monetary policy indicators had exhibited a linear upward trend for the past two decades. High volatility was observed for both the exchange rates and the short term rates. Secondly, the findings showed that the variability of foreign exchange risk premium in Kenya is influenced by a number of factors. According to a majority of the sampled key informants, changes in fiscal policies influence variations in foreign exchange risk Premium in Kenya. Government monetary and fiscal policies have an effect on the financial industry. Taxation affects the level of attractiveness of a country as destination for investments. Investors are discouraged by unbearable taxes which in turn affect a country’s foreign Forex flow. The monetary policy and changes in micro-economic variables influences the trends in the foreign exchange markets, and hence the risk premia. Thirdly, the SDF approach was applied using the multivariate GARCH-in-Mean model to show that the key determinants of foreign exchange risk premia variability in Kenya include the variability in the Treasury bill rates and the inter-bank rates. The findings from SDF analysis revealed that the other micro-economic variables under test namely Gross Domestic Debt; Total Public Debt; Total Taxation; Total Grants; Total Expenditure; NSE-20 Share Index; and the Consumer Price Index did not yield significance correlation to foreign exchange risk premium for either of the three currency series. These findings reinforce the findings earlier repor ted by the sampled key informants who reported that fiscal and monetary policies have the greatest bearing on the variability of for eign exchange risk premia in Kenya; with significant effects also coming from perceptions of the market players. The study recommends use of the applied methodology for forecasting the risk premium on a monthly basis in Kenya.