Modeling exchange rate fluctuation on tourism demand

Abstract

This research examined the effect of exchange rate fluctuations on tourism demand in Kenya using Vector Autoregressive (VAR) and Bayesian Structural Time Series (BSTS) models. Secondary data from 2010 to 2023 was sourced from the Ministry of Tourism, Wildlife & Heritage, and the Central Bank of Kenya. The VAR model revealed significant causal effects from exchange rate fluctuations, particularly the US Dollar and Euro, on regional tourism proxied by the Ugandan Shilling, with no reverse causality detected. Exogenous exchange rate shocks accounted for a substantial portion of forecast uncertainties in tourism demand. The BSTS model effectively captured trend, seasonality, and inherent uncertainty in tourism demand forecasting, with residual diagnostics confirming model validity. Forecasts demonstrated a downward trend in tourism demand over time. Comparative analysis showed the BSTS approach outperformed the VAR model, with a significantly lower Root Mean Squared Error (RMSE) of 0.0635 compared to 0.9875 for VAR and a higher forecasting efficiency ratio of 15.55. The findings indicate that major currency exchange rate fluctuations significantly affect Kenya's tourism flow. Recommendations include adopting policies for controlling exchange rate risk, incorporating the BSTS model into forecasting frameworks, monitoring economic shifts and consumer preferences, and considering external factors in modeling. By adopting these recommendations and stochastic modeling approaches, policymakers and industry players can make informed decisions on exchange rate risks, pricing strategies, and marketing to boost regional tourism.