Abstract
In the midst of macro-economic uncertainties, accurate long-term exchange rate forecasting is crucial for decision-making and planning. To measure the uncertainty associated with exchange rate and obtaining additional information of future exchange rate, a hybrid model based on quantile regression forest and Gaussian kernel (GQRF) is constructed. Quarterly dataset of KSh/USD exchange rate and macro-economic variables from 2007 to 2016 are used. The forecast horizon spans from 2013 to 2016. With a prediction interval coverage probability and prediction interval average width of 95% and 29.6493%, the constructed model has a very high coverage probability. The method of determining the probabilistic forecasts is very significant to achieve forecasts with correct coverage. The probability density forecasting model for the exchange rate gave significant information–the probability distribution of the forecasted results. In this way, uncertainties around the forecast can be evaluated because the complete exchange rate distribution are forecasted. GQRF is efficient as it can uphold the uncertainty about the variance linked to each point, which is important for exchange rate forecasting. Using the constructed model, the probabilities of exceedance such as the probability of future exchange rate exceeding the average exchange rate for the year can be computed. This paper also adds to the scarce literature of exchange rate probability density forecasting using machine learning techniques.
Highlights
Exchange rate forecasting is truly a challenging task and continues to be a very vital research area for financial institutions and economists, foreign currency hedgers, speculators, traders, and all professionals in the foreign exchange market
Governments, and stakeholders in the forex market take into accounts exchange rate forecasting to make critical and important economic decisions. ese decisions impact on the future movements of a country’s economy. e expected value of exchange rate influences cash flows of all foreign transactions
Using quarterly exchange rate and other macro-economic variables data of Kenya as a case study, we propose a probability density forecasting model that is able to capture the uncertainties associated with exchange rate. e constructed model is a hybrid model of quantile regression forest and Gaussian kernel (GQRF)
Summary
Exchange rate forecasting is truly a challenging task and continues to be a very vital research area for financial institutions and economists, foreign currency hedgers, speculators, traders, and all professionals in the foreign exchange (forex) market. The marginal profit of investing an extra unit of capital is certain, owing to the lower revenue generated by domestic firms operating locally and internationally In their studies, [6] found that exchange rate fluctuations indirectly affects domestic investment because of its effect on domestic and international trade. Using quarterly exchange rate and other macro-economic variables data of Kenya as a case study, we propose a probability density forecasting model that is able to capture the uncertainties associated with exchange rate. [11] applied a hybrid model of quantile regression forest and Epanechnikov Kernel function to capture weather uncertainties in crop yield forecasting. E contributions made in this paper are as follows: (1) a reliable, efficient, and accurate probability density forecasting model using quantile regression forest and Gaussian kernel is proposed and implemented to capture the uncertainty of exchange rate.
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