The main objectives of this research are to identify, through a systematic literature review, the potential benefits of the use of volatility models in tourism, to study the volatility of tourism demand in cities and to compare models of volatility between different destinations and source markets. The three cities analysed in Portugal were Coimbra, Lisbon and Oporto and the source markets that were studied were the domestic market, the total overnight stays, Brazil, France, Germany, Italy, Spain, the United Kingdom and other non-specified countries. The systematic review of the literature was carried out in order to identify, in a temporal perspective, the use of each methodology, variables used, data frequencies, temporal window, type of territories and geographic object of each study. The semantic analysis of the state of the art was also a methodology used. After a preliminary analysis of the time series, models that literature indicates as more suitable to estimate the volatility were used, namely, models of autoregressive conditional heteroscedasticity: ARCH, GARCH, EGARCH and TGARCH models. The most suitable models for each source market, in each city, were identified, as well as the existence of asymmetries face to positive and negative shocks, their magnitude and their persistence. Different models of volatility were identified in each city for each source market, as well as, different types of persistence of volatility, in each market and city, and different magnitude in face of good news and bad news, which strengthens the need to adjust the modelling of tourism demand for each market and, within a country, at a more detailed territorial scale. The use of volatility models is quite recent in tourism demand modelling and had not yet been applied in cities in Portugal, for which, despite the growing importance in terms of tourism, there are no studies of modelling focusing on the tourism demand. Modelling tourism demand is essential when tourism policymakers plan tourism activities. The tourism industry may be extremely sensitive to specific events’ effects, so good models must be found that reflect volatility that varies within each city and for each source market and policies must be adapted to each of the source/destination pairs.